{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning BM25 parameters\n",
    "\n",
    "We tune BM25 parameters on a per-field basis including doc2query expansions and bigrammed text fields. These values are used later when optimizing more complex queries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "import os\n",
    "import sys\n",
    "\n",
    "from copy import deepcopy\n",
    "from elasticsearch import Elasticsearch\n",
    "from skopt.plots import plot_objective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# project library\n",
    "sys.path.insert(0, os.path.abspath('..'))\n",
    "\n",
    "import qopt\n",
    "importlib.reload(qopt)\n",
    "\n",
    "from qopt.notebooks import evaluate_mrr100_dev_templated, optimize_bm25_mrr100_templated, set_bm25_params\n",
    "from qopt.optimize import Config, set_bm25_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use a local Elasticsearch or Cloud instance (https://cloud.elastic.co/)\n",
    "es = Elasticsearch('http://localhost:9200')\n",
    "\n",
    "# set the parallelization parameter `max_concurrent_searches` for the Rank Evaluation API calls\n",
    "max_concurrent_searches = 10\n",
    "\n",
    "index = 'msmarco-document.doc2query'\n",
    "template_id = 'query'\n",
    "\n",
    "# no query params\n",
    "query_params = {}\n",
    "\n",
    "# field names\n",
    "field_names = [\n",
    "    'url',\n",
    "    'title', 'title.bigrams',\n",
    "    'body', 'body.bigrams',\n",
    "    'expansions', 'expansions.bigrams'\n",
    "]\n",
    "similarity_names = [f\"bm25-{x.replace('.', '-')}\" for x in field_names]\n",
    "similarity_name_by_field = { field: similarity for field, similarity in zip(field_names, similarity_names) }\n",
    "\n",
    "# default Elasticsearch BM25 params\n",
    "default_bm25_params = {'k1': 1.2, 'b': 0.75}\n",
    "\n",
    "# base template for tuning\n",
    "base_templates = [{\n",
    "    \"id\": template_id,\n",
    "    \"template\": {\n",
    "        \"lang\": \"mustache\",\n",
    "        \"source\": { \"query\": {} }\n",
    "    }\n",
    "}]\n",
    "\n",
    "def reset_all():\n",
    "    for similarity in similarity_names:\n",
    "        set_bm25_parameters(es, index, name=similarity, **default_bm25_params)\n",
    "\n",
    "def for_all_fields(message, fn, existing_results=None):\n",
    "    _results = {}\n",
    "    for field, similarity in zip(field_names, similarity_names):\n",
    "        print(f\"{message}: {field}\")\n",
    "        if not existing_results:\n",
    "            params = default_bm25_params\n",
    "        else:\n",
    "            params = existing_results[field][1]\n",
    "        set_bm25_parameters(es, index, name=similarity, **params)\n",
    "\n",
    "        _templates = deepcopy(base_templates)\n",
    "        _templates[0]['template']['source']['query']['match'] = { field: { \"query\": \"{{query_string}}\" } }\n",
    "        _results[field] = fn(_templates, similarity)\n",
    "    return _results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dev set evaluation: url\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.2094\n",
      "Dev set evaluation: title\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.2298\n",
      "Dev set evaluation: title.bigrams\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.1295\n",
      "Dev set evaluation: body\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.2568\n",
      "Dev set evaluation: body.bigrams\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.2015\n",
      "Dev set evaluation: expansions\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.3081\n",
      "Dev set evaluation: expansions.bigrams\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.2837\n",
      "CPU times: user 15.5 s, sys: 4.3 s, total: 19.8 s\n",
      "Wall time: 13min 45s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "_ = for_all_fields(\n",
    "    \"Dev set evaluation\",\n",
    "    fn=lambda templates, similarity: evaluate_mrr100_dev_templated(es, max_concurrent_searches, index, templates, template_id, query_params),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization: url\n",
      "Optimizing parameters\n",
      " - metric: MRR@100\n",
      " - queries: data/msmarco-document-sampled-queries.1000.tsv\n",
      " - queries: data/msmarco/document/msmarco-doctrain-qrels.tsv\n",
      " > iteration 2/40, took 0:00:41 (remains: 0:26:15)\n",
      "   | 0.2013 (best: 0.2013) - {'k1': 0.9, 'b': 0.4}\n",
      " > iteration 3/40, took 0:00:07 (remains: 0:04:37)\n",
      "   | 0.1628 (best: 0.2013) - {'k1': 4.852865942208587, 'b': 0.7163140168823129}\n",
      " > iteration 4/40, took 0:00:15 (remains: 0:09:32)\n",
      "   | 0.1750 (best: 0.2013) - {'k1': 3.3057280864331884, 'b': 0.620864753693963}\n",
      " > iteration 5/40, took 0:00:08 (remains: 0:04:47)\n",
      "   | 0.1724 (best: 0.2013) - {'k1': 3.35005017291138, 'b': 0.7072841815045436}\n",
      " > iteration 6/40, took 0:00:13 (remains: 0:07:54)\n",
      "   | 0.2030 (best: 0.2030) - {'k1': 0.7324371094757943, 'b': 0.4905334540408872}\n",
      " > iteration 7/40, took 0:00:15 (remains: 0:08:33)\n",
      "   | 0.1719 (best: 0.2030) - {'k1': 3.5708710761828892, 'b': 0.7006802871661968}\n",
      " > iteration 8/40, took 0:00:08 (remains: 0:04:31)\n",
      "   | 0.1751 (best: 0.2030) - {'k1': 3.1459953179531936, 'b': 0.6644483017155304}\n",
      " > iteration 9/40, took 0:00:07 (remains: 0:03:48)\n",
      "   | 0.1756 (best: 0.2030) - {'k1': 2.4753438798505476, 'b': 0.8367447950413447}\n",
      " > iteration 10/40, took 0:00:06 (remains: 0:03:26)\n",
      "   | 0.1624 (best: 0.2030) - {'k1': 4.671709818974952, 'b': 0.3527892783552999}\n",
      " > iteration 11/40, took 0:00:06 (remains: 0:03:21)\n",
      "   | 0.1990 (best: 0.2030) - {'k1': 1.1968389341458476, 'b': 0.4895734585717294}\n",
      " > iteration 12/40, took 0:00:15 (remains: 0:07:16)\n",
      "   | 0.1636 (best: 0.2030) - {'k1': 4.913542853060431, 'b': 0.603547843201189}\n",
      " > iteration 13/40, took 0:00:14 (remains: 0:06:43)\n",
      "   | 0.1689 (best: 0.2030) - {'k1': 4.181292149148931, 'b': 0.6189522676467809}\n",
      " > iteration 14/40, took 0:00:08 (remains: 0:03:39)\n",
      "   | 0.1658 (best: 0.2030) - {'k1': 4.153865029254638, 'b': 0.4242239625075616}\n",
      " > iteration 15/40, took 0:00:07 (remains: 0:03:04)\n",
      "   | 0.1998 (best: 0.2030) - {'k1': 0.4231091839742113, 'b': 0.31914641385880793}\n",
      " > iteration 16/40, took 0:00:08 (remains: 0:03:18)\n",
      "   | 0.2083 (best: 0.2083) - {'k1': 0.2587669775415236, 'b': 0.7864281246686742}\n",
      " > iteration 17/40, took 0:00:06 (remains: 0:02:39)\n",
      "   | 0.1794 (best: 0.2083) - {'k1': 2.9568961959755837, 'b': 0.6070610249357161}\n",
      " > iteration 18/40, took 0:00:15 (remains: 0:05:49)\n",
      "   | 0.1830 (best: 0.2083) - {'k1': 2.110215356494147, 'b': 0.7940529310982194}\n",
      " > iteration 19/40, took 0:00:14 (remains: 0:05:13)\n",
      "   | 0.1614 (best: 0.2083) - {'k1': 4.525307161914916, 'b': 0.32187810226737695}\n",
      " > iteration 20/40, took 0:00:08 (remains: 0:02:50)\n",
      "   | 0.1908 (best: 0.2083) - {'k1': 1.814371494473195, 'b': 0.5849493504579577}\n",
      " > iteration 21/40, took 0:00:07 (remains: 0:02:17)\n",
      "   | 0.1787 (best: 0.2083) - {'k1': 3.0289492580971773, 'b': 0.6027248996511512}\n",
      " > iteration 22/40, took 0:00:07 (remains: 0:02:16)\n",
      "   | 0.1772 (best: 0.2083) - {'k1': 3.2347183253159635, 'b': 0.573165334659123}\n",
      " > iteration 23/40, took 0:00:10 (remains: 0:02:56)\n",
      "   | 0.1891 (best: 0.2083) - {'k1': 0.0, 'b': 1.0}\n",
      " > iteration 24/40, took 0:00:16 (remains: 0:04:30)\n",
      "   | 0.1891 (best: 0.2083) - {'k1': 0.0, 'b': 0.6525552291196847}\n",
      " > iteration 25/40, took 0:00:10 (remains: 0:02:43)\n",
      "   | 0.2038 (best: 0.2083) - {'k1': 0.5445581137299984, 'b': 1.0}\n",
      " > iteration 26/40, took 0:00:14 (remains: 0:03:27)\n",
      "   | 0.2074 (best: 0.2083) - {'k1': 0.42896785138412097, 'b': 0.7274389316939653}\n",
      " > iteration 27/40, took 0:00:15 (remains: 0:03:21)\n",
      "   | 0.1835 (best: 0.2083) - {'k1': 2.2240198776739257, 'b': 0.3004877493851064}\n",
      " > iteration 28/40, took 0:00:16 (remains: 0:03:20)\n",
      "   | 0.2083 (best: 0.2083) - {'k1': 0.33066956222950633, 'b': 0.9589101032169087}\n",
      " > iteration 29/40, took 0:00:13 (remains: 0:02:31)\n",
      "   | 0.2079 (best: 0.2083) - {'k1': 0.333743364580656, 'b': 0.8245485409865057}\n",
      " > iteration 30/40, took 0:00:09 (remains: 0:01:36)\n",
      "   | 0.1787 (best: 0.2083) - {'k1': 1.5230302210278877, 'b': 0.9999411859402323}\n",
      " > iteration 31/40, took 0:00:11 (remains: 0:01:44)\n",
      "   | 0.1975 (best: 0.2083) - {'k1': 0.8963291170553956, 'b': 0.9988349011251734}\n",
      " > iteration 32/40, took 0:00:08 (remains: 0:01:09)\n",
      "   | 0.1922 (best: 0.2083) - {'k1': 1.5041740852989363, 'b': 0.30044487185295804}\n",
      " > iteration 33/40, took 0:00:08 (remains: 0:00:59)\n",
      "   | 0.1781 (best: 0.2083) - {'k1': 2.6737262312862193, 'b': 0.3008144002653102}\n",
      " > iteration 34/40, took 0:00:16 (remains: 0:01:39)\n",
      "   | 0.1986 (best: 0.2083) - {'k1': 0.16260705243883958, 'b': 0.300080422886911}\n",
      " > iteration 35/40, took 0:00:15 (remains: 0:01:16)\n",
      "   | 0.1356 (best: 0.2083) - {'k1': 3.8937792654083836, 'b': 0.9946153738066268}\n",
      " > iteration 36/40, took 0:00:08 (remains: 0:00:34)\n",
      "   | 0.2004 (best: 0.2083) - {'k1': 0.030876903736541724, 'b': 0.4192366685818526}\n",
      " > iteration 37/40, took 0:00:07 (remains: 0:00:23)\n",
      "   | 0.2048 (best: 0.2083) - {'k1': 0.5792771144646712, 'b': 0.852826726403048}\n",
      " > iteration 38/40, took 0:00:08 (remains: 0:00:16)\n",
      "   | 0.2027 (best: 0.2083) - {'k1': 0.9146591706465236, 'b': 0.6270157710030677}\n",
      " > iteration 39/40, took 0:00:07 (remains: 0:00:07)\n",
      "   | 0.2031 (best: 0.2083) - {'k1': 0.005557574700376656, 'b': 0.8689003087855947}\n",
      " > iteration 40/40, took 0:00:07 (remains: 0:00:00)\n",
      "   | 0.1891 (best: 0.2083) - {'k1': 2.1607249105800825, 'b': 0.4474375230537646}\n",
      "Best score: 0.2083\n",
      "Best params: {'k1': 0.33066956222950633, 'b': 0.9589101032169087}\n",
      "Final params: {'k1': 0.33066956222950633, 'b': 0.9589101032169087}\n",
      "\n",
      "Optimization: title\n",
      "Optimizing parameters\n",
      " - metric: MRR@100\n",
      " - queries: data/msmarco-document-sampled-queries.1000.tsv\n",
      " - queries: data/msmarco/document/msmarco-doctrain-qrels.tsv\n",
      " > iteration 2/40, took 0:00:33 (remains: 0:21:19)\n",
      "   | 0.2057 (best: 0.2057) - {'k1': 0.9, 'b': 0.4}\n",
      " > iteration 3/40, took 0:00:08 (remains: 0:05:10)\n",
      "   | 0.2013 (best: 0.2057) - {'k1': 1.1748589885736636, 'b': 0.9132539021439572}\n",
      " > iteration 4/40, took 0:00:07 (remains: 0:04:37)\n",
      "   | 0.1997 (best: 0.2057) - {'k1': 1.9879389715000173, 'b': 0.7732671407237801}\n",
      " > iteration 5/40, took 0:00:15 (remains: 0:09:11)\n",
      "   | 0.2084 (best: 0.2084) - {'k1': 0.8441501969059302, 'b': 0.6105862144218961}\n",
      " > iteration 6/40, took 0:00:15 (remains: 0:09:02)\n",
      "   | 0.1988 (best: 0.2084) - {'k1': 2.9656606494021496, 'b': 0.5818365515267244}\n",
      " > iteration 7/40, took 0:00:15 (remains: 0:08:41)\n",
      "   | 0.2097 (best: 0.2097) - {'k1': 0.5394402229577445, 'b': 0.609940328510642}\n",
      " > iteration 8/40, took 0:00:08 (remains: 0:04:30)\n",
      "   | 0.1938 (best: 0.2097) - {'k1': 2.5467946510777932, 'b': 0.8411864249300398}\n",
      " > iteration 9/40, took 0:00:15 (remains: 0:08:07)\n",
      "   | 0.1927 (best: 0.2097) - {'k1': 4.340937675019917, 'b': 0.4327286681773639}\n",
      " > iteration 10/40, took 0:00:15 (remains: 0:07:37)\n",
      "   | 0.2067 (best: 0.2097) - {'k1': 0.7622163701183416, 'b': 0.42559232845537054}\n",
      " > iteration 11/40, took 0:00:15 (remains: 0:07:18)\n",
      "   | 0.1932 (best: 0.2097) - {'k1': 4.762914359215794, 'b': 0.6521941552774648}\n",
      " > iteration 12/40, took 0:00:17 (remains: 0:08:02)\n",
      "   | 0.1881 (best: 0.2097) - {'k1': 4.9012586332295, 'b': 0.37372226999536384}\n",
      " > iteration 13/40, took 0:00:15 (remains: 0:07:10)\n",
      "   | 0.1858 (best: 0.2097) - {'k1': 4.059493057115825, 'b': 0.8032412743525119}\n",
      " > iteration 14/40, took 0:00:15 (remains: 0:06:51)\n",
      "   | 0.2018 (best: 0.2097) - {'k1': 1.9096253656789985, 'b': 0.6421035935198338}\n",
      " > iteration 15/40, took 0:00:15 (remains: 0:06:32)\n",
      "   | 0.2088 (best: 0.2097) - {'k1': 0.6527419484945919, 'b': 0.687848664642176}\n",
      " > iteration 16/40, took 0:00:15 (remains: 0:06:09)\n",
      "   | 0.1917 (best: 0.2097) - {'k1': 3.3247899643662224, 'b': 0.3118466922603153}\n",
      " > iteration 17/40, took 0:00:15 (remains: 0:06:04)\n",
      "   | 0.1989 (best: 0.2097) - {'k1': 3.0920281290398686, 'b': 0.49872039649902666}\n",
      " > iteration 18/40, took 0:00:15 (remains: 0:05:48)\n",
      "   | 0.1909 (best: 0.2097) - {'k1': 4.303853533424812, 'b': 0.6741142491923221}\n",
      " > iteration 19/40, took 0:00:16 (remains: 0:05:38)\n",
      "   | 0.2069 (best: 0.2097) - {'k1': 0.9210141131303741, 'b': 0.7583806104451822}\n",
      " > iteration 20/40, took 0:00:14 (remains: 0:04:52)\n",
      "   | 0.2093 (best: 0.2097) - {'k1': 0.49530937617257587, 'b': 0.6720165236319717}\n",
      " > iteration 21/40, took 0:00:11 (remains: 0:03:39)\n",
      "   | 0.2070 (best: 0.2097) - {'k1': 0.9738678690014548, 'b': 0.49028146308385134}\n",
      " > iteration 22/40, took 0:00:15 (remains: 0:04:40)\n",
      "   | 0.2062 (best: 0.2097) - {'k1': 0.8216383607665718, 'b': 0.8342228554672422}\n",
      " > iteration 23/40, took 0:00:11 (remains: 0:03:12)\n",
      "   | 0.1574 (best: 0.2097) - {'k1': 0.0, 'b': 0.5571720761715984}\n",
      " > iteration 24/40, took 0:00:07 (remains: 0:02:04)\n",
      "   | 0.2002 (best: 0.2097) - {'k1': 1.510163459708175, 'b': 0.3}\n",
      " > iteration 25/40, took 0:00:16 (remains: 0:04:03)\n",
      "   | 0.1977 (best: 0.2097) - {'k1': 2.2326187806439055, 'b': 0.3}\n",
      " > iteration 26/40, took 0:00:08 (remains: 0:02:00)\n",
      "   | 0.2070 (best: 0.2097) - {'k1': 0.5854617210397388, 'b': 1.0}\n",
      " > iteration 27/40, took 0:00:17 (remains: 0:03:42)\n",
      "   | 0.1670 (best: 0.2097) - {'k1': 3.2036128313773737, 'b': 1.0}\n",
      " > iteration 28/40, took 0:00:08 (remains: 0:01:36)\n",
      "   | 0.1932 (best: 0.2097) - {'k1': 2.787910709110551, 'b': 0.3}\n",
      " > iteration 29/40, took 0:00:16 (remains: 0:03:00)\n",
      "   | 0.1444 (best: 0.2097) - {'k1': 5.0, 'b': 1.0}\n",
      " > iteration 30/40, took 0:00:18 (remains: 0:03:05)\n",
      "   | 0.1920 (best: 0.2097) - {'k1': 1.6229187073411113, 'b': 1.0}\n",
      " > iteration 31/40, took 0:00:15 (remains: 0:02:21)\n",
      "   | 0.2098 (best: 0.2098) - {'k1': 0.34885436112727763, 'b': 1.0}\n",
      " > iteration 32/40, took 0:00:07 (remains: 0:01:02)\n",
      "   | 0.1873 (best: 0.2098) - {'k1': 0.0007041819042899323, 'b': 0.9812777446717962}\n",
      " > iteration 33/40, took 0:00:07 (remains: 0:00:53)\n",
      "   | 0.1969 (best: 0.2098) - {'k1': 3.7071625782091244, 'b': 0.5477159370793313}\n",
      " > iteration 34/40, took 0:00:07 (remains: 0:00:43)\n",
      "   | 0.2023 (best: 0.2098) - {'k1': 2.393067374353989, 'b': 0.5615361710828131}\n",
      " > iteration 35/40, took 0:00:07 (remains: 0:00:35)\n",
      "   | 0.2091 (best: 0.2098) - {'k1': 0.4239682137687037, 'b': 0.9091318103017136}\n",
      " > iteration 36/40, took 0:00:15 (remains: 0:01:02)\n",
      "   | 0.2037 (best: 0.2098) - {'k1': 0.43375382153177716, 'b': 0.30059339442632116}\n",
      " > iteration 37/40, took 0:00:15 (remains: 0:00:46)\n",
      "   | 0.1871 (best: 0.2098) - {'k1': 3.8948829269785286, 'b': 0.3043451360950484}\n",
      " > iteration 38/40, took 0:00:11 (remains: 0:00:22)\n",
      "   | 0.1497 (best: 0.2098) - {'k1': 4.502637360902105, 'b': 0.9983199374041949}\n",
      " > iteration 39/40, took 0:00:08 (remains: 0:00:08)\n",
      "   | 0.1807 (best: 0.2098) - {'k1': 2.2391456638802927, 'b': 0.9982641153932903}\n",
      " > iteration 40/40, took 0:00:16 (remains: 0:00:00)\n",
      "   | 0.2067 (best: 0.2098) - {'k1': 1.5047246837407877, 'b': 0.5869483045547044}\n",
      "Best score: 0.2098\n",
      "Best params: {'k1': 0.34885436112727763, 'b': 1.0}\n",
      "Final params: {'k1': 0.34885436112727763, 'b': 1.0}\n",
      "\n",
      "Optimization: title.bigrams\n",
      "Optimizing parameters\n",
      " - metric: MRR@100\n",
      " - queries: data/msmarco-document-sampled-queries.1000.tsv\n",
      " - queries: data/msmarco/document/msmarco-doctrain-qrels.tsv\n",
      " > iteration 2/40, took 0:00:21 (remains: 0:13:54)\n",
      "   | 0.1167 (best: 0.1201) - {'k1': 0.9, 'b': 0.4}\n",
      " > iteration 3/40, took 0:00:12 (remains: 0:07:58)\n",
      "   | 0.1208 (best: 0.1208) - {'k1': 4.485479092779253, 'b': 0.5672953668155551}\n",
      " > iteration 4/40, took 0:00:08 (remains: 0:04:57)\n",
      "   | 0.1200 (best: 0.1208) - {'k1': 3.115157504694711, 'b': 0.8948497573103282}\n",
      " > iteration 5/40, took 0:00:06 (remains: 0:03:56)\n",
      "   | 0.1203 (best: 0.1208) - {'k1': 3.764383922067605, 'b': 0.7831343603525401}\n",
      " > iteration 6/40, took 0:00:14 (remains: 0:08:22)\n",
      "   | 0.1199 (best: 0.1208) - {'k1': 1.983412664520356, 'b': 0.6084603938837267}\n",
      " > iteration 7/40, took 0:00:07 (remains: 0:04:10)\n",
      "   | 0.1204 (best: 0.1208) - {'k1': 3.557199287432036, 'b': 0.5511551917857684}\n",
      " > iteration 8/40, took 0:00:08 (remains: 0:04:25)\n",
      "   | 0.1177 (best: 0.1208) - {'k1': 3.8230851623296305, 'b': 0.38510989487713687}\n",
      " > iteration 9/40, took 0:00:06 (remains: 0:03:29)\n",
      "   | 0.1198 (best: 0.1208) - {'k1': 3.6696731045840187, 'b': 0.9002532860013845}\n",
      " > iteration 10/40, took 0:00:13 (remains: 0:06:49)\n",
      "   | 0.1192 (best: 0.1208) - {'k1': 1.433999540363524, 'b': 0.3873226683873749}\n",
      " > iteration 11/40, took 0:00:14 (remains: 0:07:04)\n",
      "   | 0.1223 (best: 0.1223) - {'k1': 2.3088625061271766, 'b': 0.8325953337195824}\n",
      " > iteration 12/40, took 0:00:14 (remains: 0:06:48)\n",
      "   | 0.1212 (best: 0.1223) - {'k1': 3.90012410083342, 'b': 0.6241214921834844}\n",
      " > iteration 13/40, took 0:00:14 (remains: 0:06:41)\n",
      "   | 0.1222 (best: 0.1223) - {'k1': 1.8960468709174794, 'b': 0.8916170773088863}\n",
      " > iteration 14/40, took 0:00:07 (remains: 0:03:16)\n",
      "   | 0.1220 (best: 0.1223) - {'k1': 3.9994160862973045, 'b': 0.6979783137813953}\n",
      " > iteration 15/40, took 0:00:06 (remains: 0:02:44)\n",
      "   | 0.1211 (best: 0.1223) - {'k1': 4.58923833612909, 'b': 0.5888237350852924}\n",
      " > iteration 16/40, took 0:00:14 (remains: 0:05:55)\n",
      "   | 0.1198 (best: 0.1223) - {'k1': 4.662723570805236, 'b': 0.8042768363223605}\n",
      " > iteration 17/40, took 0:00:07 (remains: 0:03:02)\n",
      "   | 0.1212 (best: 0.1223) - {'k1': 1.6595673544400233, 'b': 0.720771340961845}\n",
      " > iteration 18/40, took 0:00:14 (remains: 0:05:28)\n",
      "   | 0.1189 (best: 0.1223) - {'k1': 1.8441395067406816, 'b': 0.4827003762231994}\n",
      " > iteration 19/40, took 0:00:16 (remains: 0:05:43)\n",
      "   | 0.1191 (best: 0.1223) - {'k1': 2.01735129066345, 'b': 0.4937564673613374}\n",
      " > iteration 20/40, took 0:00:14 (remains: 0:04:51)\n",
      "   | 0.1200 (best: 0.1223) - {'k1': 3.3535638350210557, 'b': 0.9188915965783082}\n",
      " > iteration 21/40, took 0:00:08 (remains: 0:02:46)\n",
      "   | 0.1214 (best: 0.1223) - {'k1': 4.292446003611287, 'b': 0.6175978851593993}\n",
      " > iteration 22/40, took 0:00:06 (remains: 0:02:05)\n",
      "   | 0.1156 (best: 0.1223) - {'k1': 0.5192219702276303, 'b': 0.3536030108191762}\n",
      " > iteration 23/40, took 0:00:06 (remains: 0:01:57)\n",
      "   | 0.1144 (best: 0.1223) - {'k1': 0.06247021384824582, 'b': 0.995793571321127}\n",
      " > iteration 24/40, took 0:00:06 (remains: 0:01:43)\n",
      "   | 0.1197 (best: 0.1223) - {'k1': 2.266614544750729, 'b': 1.0}\n",
      " > iteration 25/40, took 0:00:14 (remains: 0:03:44)\n",
      "   | 0.1163 (best: 0.1223) - {'k1': 4.956984165613435, 'b': 0.999090796560641}\n",
      " > iteration 26/40, took 0:00:14 (remains: 0:03:17)\n",
      "   | 0.1162 (best: 0.1223) - {'k1': 4.984251860827831, 'b': 0.3029359857618238}\n",
      " > iteration 27/40, took 0:00:13 (remains: 0:02:58)\n",
      "   | 0.1167 (best: 0.1223) - {'k1': 2.4697547613678577, 'b': 0.30051641786956584}\n",
      " > iteration 28/40, took 0:00:07 (remains: 0:01:32)\n",
      "   | 0.1215 (best: 0.1223) - {'k1': 1.8718091225625908, 'b': 0.8160144057749859}\n",
      " > iteration 29/40, took 0:00:08 (remains: 0:01:36)\n",
      "   | 0.1135 (best: 0.1223) - {'k1': 0.0025313485199252033, 'b': 0.6265876640936998}\n",
      " > iteration 30/40, took 0:00:06 (remains: 0:01:04)\n",
      "   | 0.1220 (best: 0.1223) - {'k1': 1.2006041182668146, 'b': 0.9992862867754346}\n",
      " > iteration 31/40, took 0:00:06 (remains: 0:01:02)\n",
      "   | 0.1214 (best: 0.1223) - {'k1': 2.8061930873889267, 'b': 0.64715920883775}\n",
      " > iteration 32/40, took 0:00:11 (remains: 0:01:35)\n",
      "   | 0.1224 (best: 0.1224) - {'k1': 1.5159576532570491, 'b': 0.9474530489445456}\n",
      " > iteration 33/40, took 0:00:11 (remains: 0:01:21)\n",
      "   | 0.1227 (best: 0.1227) - {'k1': 1.5077266504072955, 'b': 1.0}\n",
      " > iteration 34/40, took 0:00:14 (remains: 0:01:29)\n",
      "   | 0.1134 (best: 0.1227) - {'k1': 0.008057291422611247, 'b': 0.3016018212677284}\n",
      " > iteration 35/40, took 0:00:08 (remains: 0:00:40)\n",
      "   | 0.1227 (best: 0.1227) - {'k1': 1.5051243128436376, 'b': 1.0}\n",
      " > iteration 36/40, took 0:00:06 (remains: 0:00:27)\n",
      "   | 0.1138 (best: 0.1227) - {'k1': 0.0061622912540648675, 'b': 0.8132702539719996}\n",
      " > iteration 37/40, took 0:00:15 (remains: 0:00:45)\n",
      "   | 0.1180 (best: 0.1227) - {'k1': 4.088403293129949, 'b': 0.997904250552115}\n",
      " > iteration 38/40, took 0:00:07 (remains: 0:00:15)\n",
      "   | 0.1227 (best: 0.1227) - {'k1': 1.5027719177766896, 'b': 1.0}\n",
      " > iteration 39/40, took 0:00:16 (remains: 0:00:16)\n",
      "   | 0.1182 (best: 0.1227) - {'k1': 4.99210054583433, 'b': 0.4662850761366203}\n",
      " > iteration 40/40, took 0:00:14 (remains: 0:00:00)\n",
      "   | 0.1192 (best: 0.1227) - {'k1': 2.9506180921348686, 'b': 0.4440719387680844}\n",
      "Best score: 0.1227\n",
      "Best params: {'k1': 1.5077266504072955, 'b': 1.0}\n",
      "Final params: {'k1': 1.5077266504072955, 'b': 1.0}\n",
      "\n",
      "Optimization: body\n",
      "Optimizing parameters\n",
      " - metric: MRR@100\n",
      " - queries: data/msmarco-document-sampled-queries.1000.tsv\n",
      " - queries: data/msmarco/document/msmarco-doctrain-qrels.tsv\n",
      " > iteration 2/40, took 0:00:48 (remains: 0:30:44)\n",
      "   | 0.2042 (best: 0.2298) - {'k1': 0.9, 'b': 0.4}\n",
      " > iteration 3/40, took 0:00:08 (remains: 0:05:13)\n",
      "   | 0.2079 (best: 0.2298) - {'k1': 0.6183262116969952, 'b': 0.48176894135802134}\n",
      " > iteration 4/40, took 0:00:13 (remains: 0:07:57)\n",
      "   | 0.2391 (best: 0.2391) - {'k1': 3.748284915359279, 'b': 0.9436644069943936}\n",
      " > iteration 5/40, took 0:00:19 (remains: 0:11:37)\n",
      "   | 0.2224 (best: 0.2391) - {'k1': 1.7747799535316824, 'b': 0.5110434716299355}\n",
      " > iteration 6/40, took 0:00:18 (remains: 0:10:14)\n",
      "   | 0.2221 (best: 0.2391) - {'k1': 2.6341119099095183, 'b': 0.4933338141944413}\n",
      " > iteration 7/40, took 0:00:17 (remains: 0:09:47)\n",
      "   | 0.2161 (best: 0.2391) - {'k1': 0.5226880626192932, 'b': 0.8777377440880922}\n",
      " > iteration 8/40, took 0:00:19 (remains: 0:10:17)\n",
      "   | 0.2226 (best: 0.2391) - {'k1': 4.724974640736789, 'b': 0.5826379143279026}\n",
      " > iteration 9/40, took 0:00:12 (remains: 0:06:28)\n",
      "   | 0.1903 (best: 0.2391) - {'k1': 0.3916358099179668, 'b': 0.38053503151117685}\n",
      " > iteration 10/40, took 0:00:09 (remains: 0:04:43)\n",
      "   | 0.2189 (best: 0.2391) - {'k1': 2.749006734531953, 'b': 0.44748532957624276}\n",
      " > iteration 11/40, took 0:00:17 (remains: 0:08:34)\n",
      "   | 0.2060 (best: 0.2391) - {'k1': 2.1580559945693274, 'b': 0.33036389675779176}\n",
      " > iteration 12/40, took 0:00:17 (remains: 0:08:18)\n",
      "   | 0.2237 (best: 0.2391) - {'k1': 4.6848893355099746, 'b': 0.5943987447643887}\n",
      " > iteration 13/40, took 0:00:09 (remains: 0:04:29)\n",
      "   | 0.2099 (best: 0.2391) - {'k1': 1.0354153406994413, 'b': 0.4222194877278328}\n",
      " > iteration 14/40, took 0:00:14 (remains: 0:06:08)\n",
      "   | 0.2344 (best: 0.2391) - {'k1': 3.8820680652978012, 'b': 0.990296506132835}\n",
      " > iteration 15/40, took 0:00:09 (remains: 0:04:00)\n",
      "   | 0.2359 (best: 0.2391) - {'k1': 2.3956304830007547, 'b': 0.6534880085618948}\n",
      " > iteration 16/40, took 0:00:18 (remains: 0:07:23)\n",
      "   | 0.2109 (best: 0.2391) - {'k1': 0.39173690454522575, 'b': 0.7600700695081284}\n",
      " > iteration 17/40, took 0:00:20 (remains: 0:07:41)\n",
      "   | 0.2398 (best: 0.2398) - {'k1': 4.563334018601294, 'b': 0.8166559454228937}\n",
      " > iteration 18/40, took 0:00:10 (remains: 0:04:01)\n",
      "   | 0.2011 (best: 0.2398) - {'k1': 0.12783325274942473, 'b': 0.7376121087713838}\n",
      " > iteration 19/40, took 0:00:09 (remains: 0:03:13)\n",
      "   | 0.2202 (best: 0.2398) - {'k1': 1.8353110159470234, 'b': 0.491940249748945}\n",
      " > iteration 20/40, took 0:00:08 (remains: 0:02:49)\n",
      "   | 0.2365 (best: 0.2398) - {'k1': 2.31594456771881, 'b': 0.8633859736475613}\n",
      " > iteration 21/40, took 0:00:08 (remains: 0:02:42)\n",
      "   | 0.2351 (best: 0.2398) - {'k1': 3.173132516962271, 'b': 0.639194567753254}\n",
      " > iteration 22/40, took 0:00:09 (remains: 0:02:42)\n",
      "   | 0.2016 (best: 0.2398) - {'k1': 2.226195079407944, 'b': 0.30346511149702393}\n",
      " > iteration 23/40, took 0:00:14 (remains: 0:04:14)\n",
      "   | 0.2410 (best: 0.2410) - {'k1': 3.575734657450034, 'b': 0.8079672939155187}\n",
      " > iteration 24/40, took 0:00:11 (remains: 0:02:56)\n",
      "   | 0.2376 (best: 0.2410) - {'k1': 5.0, 'b': 0.9747692350957902}\n",
      " > iteration 25/40, took 0:00:18 (remains: 0:04:33)\n",
      "   | 0.1942 (best: 0.2410) - {'k1': 5.0, 'b': 0.3}\n",
      " > iteration 26/40, took 0:00:11 (remains: 0:02:34)\n",
      "   | 0.2246 (best: 0.2410) - {'k1': 1.7248398802865952, 'b': 0.9995139325708571}\n",
      " > iteration 27/40, took 0:00:17 (remains: 0:03:47)\n",
      "   | 0.2367 (best: 0.2410) - {'k1': 4.986297866195325, 'b': 0.8047966494597247}\n",
      " > iteration 28/40, took 0:00:17 (remains: 0:03:28)\n",
      "   | 0.2394 (best: 0.2410) - {'k1': 3.998767351094083, 'b': 0.8375665908143091}\n",
      " > iteration 29/40, took 0:00:09 (remains: 0:01:39)\n",
      "   | 0.2396 (best: 0.2410) - {'k1': 3.029802621068251, 'b': 0.8507895160207595}\n",
      " > iteration 30/40, took 0:00:12 (remains: 0:02:09)\n",
      "   | 0.1928 (best: 0.2410) - {'k1': 0.04422428490364783, 'b': 0.9947254905278533}\n",
      " > iteration 31/40, took 0:00:09 (remains: 0:01:21)\n",
      "   | 0.2404 (best: 0.2410) - {'k1': 3.3595516539752, 'b': 0.8057615530476072}\n",
      " > iteration 32/40, took 0:00:16 (remains: 0:02:12)\n",
      "   | 0.2415 (best: 0.2415) - {'k1': 3.4418843002060036, 'b': 0.8106120134638413}\n",
      " > iteration 33/40, took 0:00:09 (remains: 0:01:07)\n",
      "   | 0.2303 (best: 0.2415) - {'k1': 2.918536870479053, 'b': 0.9983169084488823}\n",
      " > iteration 34/40, took 0:00:08 (remains: 0:00:50)\n",
      "   | 0.2323 (best: 0.2415) - {'k1': 4.548564493720921, 'b': 0.9983477430299839}\n",
      " > iteration 35/40, took 0:00:18 (remains: 0:01:31)\n",
      "   | 0.2411 (best: 0.2415) - {'k1': 3.5548186851884465, 'b': 0.8158599562328299}\n",
      " > iteration 36/40, took 0:00:11 (remains: 0:00:44)\n",
      "   | 0.1996 (best: 0.2415) - {'k1': 3.8385703616741074, 'b': 0.30511440226047903}\n",
      " > iteration 37/40, took 0:00:09 (remains: 0:00:27)\n",
      "   | 0.2412 (best: 0.2415) - {'k1': 3.5180252050075995, 'b': 0.8157377339518241}\n",
      " > iteration 38/40, took 0:00:08 (remains: 0:00:16)\n",
      "   | 0.2405 (best: 0.2415) - {'k1': 3.2864478002860102, 'b': 0.8051988280870099}\n",
      " > iteration 39/40, took 0:00:08 (remains: 0:00:08)\n",
      "   | 0.2404 (best: 0.2415) - {'k1': 3.6918524168140103, 'b': 0.824890848441477}\n",
      " > iteration 40/40, took 0:00:08 (remains: 0:00:00)\n",
      "   | 0.2411 (best: 0.2415) - {'k1': 3.2318769978691835, 'b': 0.803049782759234}\n",
      "Best score: 0.2415\n",
      "Best params: {'k1': 3.4418843002060036, 'b': 0.8106120134638413}\n",
      "Final params: {'k1': 3.4418843002060036, 'b': 0.8106120134638413}\n",
      "\n",
      "Optimization: body.bigrams\n",
      "Optimizing parameters\n",
      " - metric: MRR@100\n",
      " - queries: data/msmarco-document-sampled-queries.1000.tsv\n",
      " - queries: data/msmarco/document/msmarco-doctrain-qrels.tsv\n",
      " > iteration 2/40, took 0:01:20 (remains: 0:50:52)\n",
      "   | 0.1805 (best: 0.1858) - {'k1': 0.9, 'b': 0.4}\n",
      " > iteration 3/40, took 0:00:31 (remains: 0:19:22)\n",
      "   | 0.1858 (best: 0.1858) - {'k1': 3.0859545334362055, 'b': 0.7495861402721271}\n",
      " > iteration 4/40, took 0:00:21 (remains: 0:13:08)\n",
      "   | 0.1741 (best: 0.1858) - {'k1': 3.945432483917048, 'b': 0.9985897837261513}\n",
      " > iteration 5/40, took 0:00:23 (remains: 0:13:46)\n",
      "   | 0.1815 (best: 0.1858) - {'k1': 3.2600544208652655, 'b': 0.4078898893097473}\n",
      " > iteration 6/40, took 0:00:19 (remains: 0:10:48)\n",
      "   | 0.1886 (best: 0.1886) - {'k1': 2.743492086079958, 'b': 0.7389706497087632}\n",
      " > iteration 7/40, took 0:00:21 (remains: 0:12:00)\n",
      "   | 0.1678 (best: 0.1886) - {'k1': 0.17830643628697687, 'b': 0.5695288571880839}\n",
      " > iteration 8/40, took 0:00:19 (remains: 0:10:33)\n",
      "   | 0.1677 (best: 0.1886) - {'k1': 0.1825615531173436, 'b': 0.7141699837577988}\n",
      " > iteration 9/40, took 0:00:29 (remains: 0:15:07)\n",
      "   | 0.1895 (best: 0.1895) - {'k1': 1.9753972218229798, 'b': 0.8344316086149068}\n",
      " > iteration 10/40, took 0:00:17 (remains: 0:08:58)\n",
      "   | 0.1861 (best: 0.1895) - {'k1': 2.846104727556016, 'b': 0.6414806788917581}\n",
      " > iteration 11/40, took 0:00:36 (remains: 0:17:50)\n",
      "   | 0.1833 (best: 0.1895) - {'k1': 2.933477766047807, 'b': 0.5744649922283651}\n",
      " > iteration 12/40, took 0:00:26 (remains: 0:12:30)\n",
      "   | 0.1900 (best: 0.1900) - {'k1': 1.6547861822646694, 'b': 0.7360423352153108}\n",
      " > iteration 13/40, took 0:00:30 (remains: 0:13:55)\n",
      "   | 0.1568 (best: 0.1900) - {'k1': 0.016750829132485826, 'b': 0.47846926712302607}\n",
      " > iteration 14/40, took 0:00:28 (remains: 0:12:31)\n",
      "   | 0.1831 (best: 0.1900) - {'k1': 0.9186239409404191, 'b': 0.8168269642719279}\n",
      " > iteration 15/40, took 0:00:29 (remains: 0:12:26)\n",
      "   | 0.1847 (best: 0.1900) - {'k1': 2.6540575338587273, 'b': 0.5688388803976208}\n",
      " > iteration 16/40, took 0:00:12 (remains: 0:05:07)\n",
      "   | 0.1873 (best: 0.1900) - {'k1': 2.0638177140295673, 'b': 0.5524043203361658}\n",
      " > iteration 17/40, took 0:00:07 (remains: 0:02:59)\n",
      "   | 0.1792 (best: 0.1900) - {'k1': 4.945791742888704, 'b': 0.6151539556555536}\n",
      " > iteration 18/40, took 0:00:07 (remains: 0:02:51)\n",
      "   | 0.1842 (best: 0.1900) - {'k1': 2.016317531796762, 'b': 0.433966612959082}\n",
      " > iteration 19/40, took 0:00:16 (remains: 0:05:48)\n",
      "   | 0.1774 (best: 0.1900) - {'k1': 3.581404575394081, 'b': 0.30237922115381116}\n",
      " > iteration 20/40, took 0:00:15 (remains: 0:05:14)\n",
      "   | 0.1787 (best: 0.1900) - {'k1': 4.742660806513108, 'b': 0.8343675571031695}\n",
      " > iteration 21/40, took 0:00:17 (remains: 0:05:29)\n",
      "   | 0.1755 (best: 0.1900) - {'k1': 0.5730318782407363, 'b': 0.924668500895669}\n",
      " > iteration 22/40, took 0:00:21 (remains: 0:06:21)\n",
      "   | 0.1855 (best: 0.1900) - {'k1': 1.4550152897422972, 'b': 0.9006224501628057}\n",
      " > iteration 23/40, took 0:00:15 (remains: 0:04:22)\n",
      "   | 0.1810 (best: 0.1900) - {'k1': 2.3123170129072204, 'b': 0.9966298895328496}\n",
      " > iteration 24/40, took 0:00:15 (remains: 0:04:04)\n",
      "   | 0.1804 (best: 0.1900) - {'k1': 1.629424502721689, 'b': 0.3}\n",
      " > iteration 25/40, took 0:00:19 (remains: 0:04:55)\n",
      "   | 0.1706 (best: 0.1900) - {'k1': 4.995852274979443, 'b': 0.30159264764590826}\n",
      " > iteration 26/40, took 0:00:28 (remains: 0:06:40)\n",
      "   | 0.1724 (best: 0.1900) - {'k1': 4.99245226686598, 'b': 0.9872102496792576}\n",
      " > iteration 27/40, took 0:00:28 (remains: 0:06:08)\n",
      "   | 0.1883 (best: 0.1900) - {'k1': 1.9026871280572821, 'b': 0.710420601714676}\n",
      " > iteration 28/40, took 0:00:13 (remains: 0:02:40)\n",
      "   | 0.1818 (best: 0.1900) - {'k1': 4.005412673905941, 'b': 0.5945296526346645}\n",
      " > iteration 29/40, took 0:00:35 (remains: 0:06:31)\n",
      "   | 0.1489 (best: 0.1900) - {'k1': 0.006701119025394054, 'b': 0.9994307111932239}\n",
      " > iteration 30/40, took 0:00:30 (remains: 0:05:04)\n",
      "   | 0.1892 (best: 0.1900) - {'k1': 1.6433259522924466, 'b': 0.6784607541380507}\n",
      " > iteration 31/40, took 0:00:30 (remains: 0:04:35)\n",
      "   | 0.1778 (best: 0.1900) - {'k1': 2.4852401292228166, 'b': 0.3026623620852234}\n",
      " > iteration 32/40, took 0:00:27 (remains: 0:03:36)\n",
      "   | 0.1814 (best: 0.1900) - {'k1': 1.5405086040902796, 'b': 0.9995274570490673}\n",
      " > iteration 33/40, took 0:00:21 (remains: 0:02:27)\n",
      "   | 0.1854 (best: 0.1900) - {'k1': 1.2747003168041513, 'b': 0.5585832051411725}\n",
      " > iteration 34/40, took 0:00:27 (remains: 0:02:47)\n",
      "   | 0.1786 (best: 0.1900) - {'k1': 3.00589055256522, 'b': 0.9992886600379809}\n",
      " > iteration 35/40, took 0:00:33 (remains: 0:02:49)\n",
      "   | 0.1744 (best: 0.1900) - {'k1': 4.3538160044754495, 'b': 0.3027348683410403}\n",
      " > iteration 36/40, took 0:00:30 (remains: 0:02:01)\n",
      "   | 0.1897 (best: 0.1900) - {'k1': 2.0255271813412716, 'b': 0.7365263311184367}\n",
      " > iteration 37/40, took 0:00:10 (remains: 0:00:31)\n",
      "   | 0.1895 (best: 0.1900) - {'k1': 2.0633802311631055, 'b': 0.7392302859549982}\n",
      " > iteration 38/40, took 0:00:07 (remains: 0:00:14)\n",
      "   | 0.1894 (best: 0.1900) - {'k1': 2.067734905586823, 'b': 0.7397109643205351}\n",
      " > iteration 39/40, took 0:00:22 (remains: 0:00:22)\n",
      "   | 0.1764 (best: 0.1900) - {'k1': 0.6310826639152907, 'b': 0.30069956152227834}\n",
      " > iteration 40/40, took 0:00:29 (remains: 0:00:00)\n",
      "   | 0.1904 (best: 0.1904) - {'k1': 1.9100199633100623, 'b': 0.7336619962002098}\n",
      "Best score: 0.1904\n",
      "Best params: {'k1': 1.9100199633100623, 'b': 0.7336619962002098}\n",
      "Final params: {'k1': 1.9100199633100623, 'b': 0.7336619962002098}\n",
      "\n",
      "Optimization: expansions\n",
      "Optimizing parameters\n",
      " - metric: MRR@100\n",
      " - queries: data/msmarco-document-sampled-queries.1000.tsv\n",
      " - queries: data/msmarco/document/msmarco-doctrain-qrels.tsv\n",
      " > iteration 2/40, took 0:00:49 (remains: 0:31:39)\n",
      "   | 0.2665 (best: 0.2804) - {'k1': 0.9, 'b': 0.4}\n",
      " > iteration 3/40, took 0:00:17 (remains: 0:11:03)\n",
      "   | 0.2951 (best: 0.2951) - {'k1': 4.821634892805274, 'b': 0.9992043646380031}\n",
      " > iteration 4/40, took 0:00:16 (remains: 0:09:41)\n",
      "   | 0.2879 (best: 0.2951) - {'k1': 1.1712380592726348, 'b': 0.8682257062613443}\n",
      " > iteration 5/40, took 0:00:08 (remains: 0:04:54)\n",
      "   | 0.2749 (best: 0.2951) - {'k1': 2.030885711123376, 'b': 0.5655596350035239}\n",
      " > iteration 6/40, took 0:00:07 (remains: 0:04:21)\n",
      "   | 0.2879 (best: 0.2951) - {'k1': 4.046696499799916, 'b': 0.7030766439900421}\n",
      " > iteration 7/40, took 0:00:07 (remains: 0:03:59)\n",
      "   | 0.2817 (best: 0.2951) - {'k1': 2.4321112295009395, 'b': 0.6205766348649775}\n",
      " > iteration 8/40, took 0:00:06 (remains: 0:03:37)\n",
      "   | 0.2698 (best: 0.2951) - {'k1': 1.1573802483244657, 'b': 0.5247010102362706}\n",
      " > iteration 9/40, took 0:00:07 (remains: 0:03:41)\n",
      "   | 0.2671 (best: 0.2951) - {'k1': 0.8171548766213239, 'b': 0.5726514081300811}\n",
      " > iteration 10/40, took 0:00:14 (remains: 0:07:20)\n",
      "   | 0.2935 (best: 0.2951) - {'k1': 2.6922486992319437, 'b': 0.8428618541033706}\n",
      " > iteration 11/40, took 0:00:08 (remains: 0:04:16)\n",
      "   | 0.2676 (best: 0.2951) - {'k1': 1.5645756575227412, 'b': 0.3619764411598218}\n",
      " > iteration 12/40, took 0:00:15 (remains: 0:07:00)\n",
      "   | 0.2804 (best: 0.2951) - {'k1': 3.6266287760193805, 'b': 0.55217215623938}\n",
      " > iteration 13/40, took 0:00:14 (remains: 0:06:40)\n",
      "   | 0.2585 (best: 0.2951) - {'k1': 0.7227503209359, 'b': 0.3258067771473695}\n",
      " > iteration 14/40, took 0:00:08 (remains: 0:03:49)\n",
      "   | 0.2937 (best: 0.2951) - {'k1': 2.19908083668676, 'b': 0.9466479812288169}\n",
      " > iteration 15/40, took 0:00:15 (remains: 0:06:27)\n",
      "   | 0.2768 (best: 0.2951) - {'k1': 2.8547273037530596, 'b': 0.397507985549981}\n",
      " > iteration 16/40, took 0:00:14 (remains: 0:05:49)\n",
      "   | 0.2885 (best: 0.2951) - {'k1': 1.7841504557184737, 'b': 0.7756155110892008}\n",
      " > iteration 17/40, took 0:00:12 (remains: 0:04:57)\n",
      "   | 0.2627 (best: 0.2951) - {'k1': 0.20678494388348792, 'b': 0.7576154240072448}\n",
      " > iteration 18/40, took 0:00:09 (remains: 0:03:27)\n",
      "   | 0.2764 (best: 0.2951) - {'k1': 3.1278191671048217, 'b': 0.3688099057606903}\n",
      " > iteration 19/40, took 0:00:09 (remains: 0:03:25)\n",
      "   | 0.2791 (best: 0.2951) - {'k1': 1.1610471107413758, 'b': 0.7455877621419051}\n",
      " > iteration 20/40, took 0:00:13 (remains: 0:04:27)\n",
      "   | 0.2799 (best: 0.2951) - {'k1': 1.8820735727902909, 'b': 0.6374384608318684}\n",
      " > iteration 21/40, took 0:00:08 (remains: 0:02:45)\n",
      "   | 0.2690 (best: 0.2951) - {'k1': 3.72157325918054, 'b': 0.3100659969723847}\n",
      " > iteration 22/40, took 0:00:08 (remains: 0:02:27)\n",
      "   | 0.2966 (best: 0.2966) - {'k1': 3.13463411217811, 'b': 0.8383994126723253}\n",
      " > iteration 23/40, took 0:00:07 (remains: 0:02:10)\n",
      "   | 0.2955 (best: 0.2966) - {'k1': 3.6488287288846344, 'b': 1.0}\n",
      " > iteration 24/40, took 0:00:07 (remains: 0:01:56)\n",
      "   | 0.3020 (best: 0.3020) - {'k1': 5.0, 'b': 0.8561082699301825}\n",
      " > iteration 25/40, took 0:00:11 (remains: 0:02:54)\n",
      "   | 0.1199 (best: 0.3020) - {'k1': 0.0, 'b': 1.0}\n",
      " > iteration 26/40, took 0:00:08 (remains: 0:02:01)\n",
      "   | 0.2737 (best: 0.3020) - {'k1': 5.0, 'b': 0.4497042250899949}\n",
      " > iteration 27/40, took 0:00:08 (remains: 0:01:52)\n",
      "   | 0.3015 (best: 0.3020) - {'k1': 4.1689530355649955, 'b': 0.9027826715354583}\n",
      " > iteration 28/40, took 0:00:14 (remains: 0:02:49)\n",
      "   | 0.2935 (best: 0.3020) - {'k1': 5.0, 'b': 0.7466661824293201}\n",
      " > iteration 29/40, took 0:00:09 (remains: 0:01:42)\n",
      "   | 0.2978 (best: 0.3020) - {'k1': 4.324412722283606, 'b': 0.8207220369469015}\n",
      " > iteration 30/40, took 0:00:07 (remains: 0:01:19)\n",
      "   | 0.2666 (best: 0.3020) - {'k1': 5.0, 'b': 0.3}\n",
      " > iteration 31/40, took 0:00:07 (remains: 0:01:04)\n",
      "   | 0.3010 (best: 0.3020) - {'k1': 3.2344553593991683, 'b': 0.9283805044772957}\n",
      " > iteration 32/40, took 0:00:07 (remains: 0:00:58)\n",
      "   | 0.2838 (best: 0.3020) - {'k1': 5.0, 'b': 0.6054471031654505}\n",
      " > iteration 33/40, took 0:00:07 (remains: 0:00:50)\n",
      "   | 0.3064 (best: 0.3064) - {'k1': 5.0, 'b': 0.9190886566031851}\n",
      " > iteration 34/40, took 0:00:07 (remains: 0:00:43)\n",
      "   | 0.3025 (best: 0.3064) - {'k1': 5.0, 'b': 0.9337353442234138}\n",
      " > iteration 35/40, took 0:00:07 (remains: 0:00:36)\n",
      "   | 0.3032 (best: 0.3064) - {'k1': 5.0, 'b': 0.8998792085481424}\n",
      " > iteration 36/40, took 0:00:08 (remains: 0:00:34)\n",
      "   | 0.2484 (best: 0.3064) - {'k1': 0.0007008325909318992, 'b': 0.47229305517046827}\n",
      " > iteration 37/40, took 0:00:07 (remains: 0:00:21)\n",
      "   | 0.3064 (best: 0.3064) - {'k1': 4.631064048169288, 'b': 0.9207046408403603}\n",
      " > iteration 38/40, took 0:00:09 (remains: 0:00:19)\n",
      "   | 0.3039 (best: 0.3064) - {'k1': 4.499516381190682, 'b': 0.9294185901964866}\n",
      " > iteration 39/40, took 0:00:07 (remains: 0:00:07)\n",
      "   | 0.2428 (best: 0.3064) - {'k1': 0.011754849784484935, 'b': 0.3014360521764926}\n",
      " > iteration 40/40, took 0:00:07 (remains: 0:00:00)\n",
      "   | 0.3063 (best: 0.3064) - {'k1': 4.686188755999396, 'b': 0.9136957977785161}\n",
      "Best score: 0.3064\n",
      "Best params: {'k1': 5.0, 'b': 0.9190886566031851}\n",
      "Final params: {'k1': 5.0, 'b': 0.9190886566031851}\n",
      "\n",
      "Optimization: expansions.bigrams\n",
      "Optimizing parameters\n",
      " - metric: MRR@100\n",
      " - queries: data/msmarco-document-sampled-queries.1000.tsv\n",
      " - queries: data/msmarco/document/msmarco-doctrain-qrels.tsv\n",
      " > iteration 2/40, took 0:00:36 (remains: 0:23:14)\n",
      "   | 0.2567 (best: 0.2685) - {'k1': 0.9, 'b': 0.4}\n",
      " > iteration 3/40, took 0:00:07 (remains: 0:04:26)\n",
      "   | 0.2638 (best: 0.2685) - {'k1': 1.886413966168815, 'b': 0.6563317608617942}\n",
      " > iteration 4/40, took 0:00:06 (remains: 0:04:09)\n",
      "   | 0.2614 (best: 0.2685) - {'k1': 3.397207350611268, 'b': 0.7180578348580084}\n",
      " > iteration 5/40, took 0:00:07 (remains: 0:04:35)\n",
      "   | 0.2521 (best: 0.2685) - {'k1': 2.051907741538281, 'b': 0.3166452419857358}\n",
      " > iteration 6/40, took 0:00:06 (remains: 0:03:36)\n",
      "   | 0.2669 (best: 0.2685) - {'k1': 1.0642139669170878, 'b': 0.7069135397251006}\n",
      " > iteration 7/40, took 0:00:06 (remains: 0:03:26)\n",
      "   | 0.2628 (best: 0.2685) - {'k1': 1.3708335120679136, 'b': 0.5940795689623406}\n",
      " > iteration 8/40, took 0:00:06 (remains: 0:03:26)\n",
      "   | 0.2611 (best: 0.2685) - {'k1': 2.1572649166435687, 'b': 0.5107278563121784}\n",
      " > iteration 9/40, took 0:00:06 (remains: 0:03:21)\n",
      "   | 0.2548 (best: 0.2685) - {'k1': 2.2921341678317857, 'b': 0.4316368214207633}\n",
      " > iteration 10/40, took 0:00:06 (remains: 0:03:15)\n",
      "   | 0.2674 (best: 0.2685) - {'k1': 1.4086145592257773, 'b': 0.8029605827312976}\n",
      " > iteration 11/40, took 0:00:06 (remains: 0:03:14)\n",
      "   | 0.2613 (best: 0.2685) - {'k1': 1.6501238811827037, 'b': 0.585241259764547}\n",
      " > iteration 12/40, took 0:00:12 (remains: 0:05:39)\n",
      "   | 0.2630 (best: 0.2685) - {'k1': 0.5312652698516513, 'b': 0.803843964047041}\n",
      " > iteration 13/40, took 0:00:08 (remains: 0:04:02)\n",
      "   | 0.2527 (best: 0.2685) - {'k1': 3.797041730222711, 'b': 0.579953722534452}\n",
      " > iteration 14/40, took 0:00:08 (remains: 0:03:31)\n",
      "   | 0.2603 (best: 0.2685) - {'k1': 0.8431983052972165, 'b': 0.9716273001390818}\n",
      " > iteration 15/40, took 0:00:09 (remains: 0:04:02)\n",
      "   | 0.2572 (best: 0.2685) - {'k1': 4.372300599278071, 'b': 0.8035436918288306}\n",
      " > iteration 16/40, took 0:00:06 (remains: 0:02:46)\n",
      "   | 0.2616 (best: 0.2685) - {'k1': 1.101357124807151, 'b': 0.5849871448000868}\n",
      " > iteration 17/40, took 0:00:06 (remains: 0:02:40)\n",
      "   | 0.2628 (best: 0.2685) - {'k1': 3.155440715322025, 'b': 0.7168498328815109}\n",
      " > iteration 18/40, took 0:00:06 (remains: 0:02:29)\n",
      "   | 0.2632 (best: 0.2685) - {'k1': 3.258946442106503, 'b': 0.7537115657232605}\n",
      " > iteration 19/40, took 0:00:06 (remains: 0:02:21)\n",
      "   | 0.2438 (best: 0.2685) - {'k1': 4.307123656932904, 'b': 0.32501530848895277}\n",
      " > iteration 20/40, took 0:00:10 (remains: 0:03:26)\n",
      "   | 0.2649 (best: 0.2685) - {'k1': 2.606156063504687, 'b': 0.7502809073793184}\n",
      " > iteration 21/40, took 0:00:08 (remains: 0:02:39)\n",
      "   | 0.2460 (best: 0.2685) - {'k1': 4.212393251423495, 'b': 0.3614090145904947}\n",
      " > iteration 22/40, took 0:00:08 (remains: 0:02:25)\n",
      "   | 0.2645 (best: 0.2685) - {'k1': 2.5077279388357896, 'b': 0.6243380813739405}\n",
      " > iteration 23/40, took 0:00:07 (remains: 0:02:03)\n",
      "   | 0.2551 (best: 0.2685) - {'k1': 3.042430351897241, 'b': 0.999741128482311}\n",
      " > iteration 24/40, took 0:00:08 (remains: 0:02:16)\n",
      "   | 0.2483 (best: 0.2685) - {'k1': 5.0, 'b': 1.0}\n",
      " > iteration 25/40, took 0:00:06 (remains: 0:01:42)\n",
      "   | 0.2681 (best: 0.2685) - {'k1': 1.764080639590606, 'b': 0.7765195363928783}\n",
      " > iteration 26/40, took 0:00:06 (remains: 0:01:36)\n",
      "   | 0.2429 (best: 0.2685) - {'k1': 0.015193083701502966, 'b': 0.3058215209185693}\n",
      " > iteration 27/40, took 0:00:06 (remains: 0:01:25)\n",
      "   | 0.2485 (best: 0.2685) - {'k1': 0.00036946260646811106, 'b': 0.620456231998523}\n",
      " > iteration 28/40, took 0:00:10 (remains: 0:02:04)\n",
      "   | 0.1785 (best: 0.2685) - {'k1': 0.0, 'b': 1.0}\n",
      " > iteration 29/40, took 0:00:07 (remains: 0:01:22)\n",
      "   | 0.2487 (best: 0.2685) - {'k1': 5.0, 'b': 0.5794471566357813}\n",
      " > iteration 30/40, took 0:00:13 (remains: 0:02:18)\n",
      "   | 0.2610 (best: 0.2685) - {'k1': 1.883464096109715, 'b': 1.0}\n",
      " > iteration 31/40, took 0:00:11 (remains: 0:01:47)\n",
      "   | 0.2536 (best: 0.2685) - {'k1': 3.9486490947586974, 'b': 1.0}\n",
      " > iteration 32/40, took 0:00:09 (remains: 0:01:12)\n",
      "   | 0.2472 (best: 0.2685) - {'k1': 3.2881159919375103, 'b': 0.3}\n",
      " > iteration 33/40, took 0:00:09 (remains: 0:01:03)\n",
      "   | 0.2670 (best: 0.2685) - {'k1': 0.9953255899231838, 'b': 0.8407327119545256}\n",
      " > iteration 34/40, took 0:00:07 (remains: 0:00:43)\n",
      "   | 0.2576 (best: 0.2685) - {'k1': 0.5070171162478161, 'b': 0.5830539627846227}\n",
      " > iteration 35/40, took 0:00:11 (remains: 0:00:57)\n",
      "   | 0.2541 (best: 0.2685) - {'k1': 1.3411360395060186, 'b': 0.3}\n",
      " > iteration 36/40, took 0:00:09 (remains: 0:00:36)\n",
      "   | 0.2409 (best: 0.2685) - {'k1': 4.961880191249495, 'b': 0.30697385327439203}\n",
      " > iteration 37/40, took 0:00:08 (remains: 0:00:24)\n",
      "   | 0.2613 (best: 0.2685) - {'k1': 1.3583421742664046, 'b': 0.9970255733050968}\n",
      " > iteration 38/40, took 0:00:07 (remains: 0:00:14)\n",
      "   | 0.2654 (best: 0.2685) - {'k1': 2.21611355302289, 'b': 0.8666417333187497}\n",
      " > iteration 39/40, took 0:00:07 (remains: 0:00:07)\n",
      "   | 0.2690 (best: 0.2690) - {'k1': 1.8413006685653024, 'b': 0.8445599330786397}\n",
      " > iteration 40/40, took 0:00:06 (remains: 0:00:00)\n",
      "   | 0.2525 (best: 0.2690) - {'k1': 5.0, 'b': 0.806240071812361}\n",
      "Best score: 0.2690\n",
      "Best params: {'k1': 1.8413006685653024, 'b': 0.8445599330786397}\n",
      "Final params: {'k1': 1.8413006685653024, 'b': 0.8445599330786397}\n",
      "\n",
      "CPU times: user 4min 34s, sys: 1min 7s, total: 5min 42s\n",
      "Wall time: 1h 1min 39s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "results = for_all_fields(\n",
    "    \"Optimization\",\n",
    "    fn=lambda templates, similarity: optimize_bm25_mrr100_templated(es, max_concurrent_searches, index, templates, template_id, query_params,\n",
    "        config_space=Config.parse({\n",
    "            'method': 'bayesian',\n",
    "            'num_iterations': 40,\n",
    "            'num_initial_points': 20,\n",
    "            'space': {\n",
    "                'k1': { 'low': 0.0, 'high': 5.0 },\n",
    "                'b': { 'low': 0.3, 'high': 1.0 },\n",
    "            }\n",
    "        }),\n",
    "        name=similarity),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jddOM+IuDt2/vtQVGhFHVrqraDXgbGKuqOaqaDYwBZnprneEHkk7ARYSh3VqzcPUONJ6qwG+2Ai0+ZqiqvlF1oapvAid6aI/hE5JOwMGJgxftOsj6eIuDG36lSERuE5Eu7nErYOknjQaTnALePQ7Xgw8c6LUFRvS4GMjFWUr4int+sacWGb4g6SYxAbrnNiO3eSM+WLODcYPzvTbHobDQawuMKKGqJcD1Xtth+I+k9MBFhBO6ZTN/5XbKK+JkQ9ykSV5bYBhGgpGUAg5wRr927NhXyodr4yS51RNPeG1BwvD+6u1em2AYcUHSCvgpfdrQrFEary+1uaREobS8kslvfsWPnvywQc9xN9fMEpGV7murGtrNEJGdIjK92v2RIrJYRJaIyHwR6dEggwyjniRlDBwgMz2V0/u25c3Pt3DXOf3ISEva77KEYM22vVz/whI+27SLiwd3YnLDHnczMFtVJ4vIze71TUHa3Qc0Aa6sdv9R4BxVXSYiVwO3AZdV7ywiD+MmsAqGql5XP/MNwyFpBRxg7LHteeWTTcxftY1T+7T11phNm7wdP07Zd6icx+euZsq8NWSmp/LYJYMY3a9dQwX8HGCEez4VeJcgAq6qs0VkRPX7OKJcVUkni5qXBFqSdyOqJLWAn9Qjh5ZN0nl96WbvBbyw0HZjBlBWUcmrizdx/8zlFO85xNhj23PrmUfRLiszEo9vq6pVO6e2AOH+518BvCEiB4DdwNBgjVR1av1NNIy6SWgBF5FJwCSA/PzwlwNmpKVwRr92TFtSxMGyCjLTUyNtYuicfTbE085Qj9i+9xDPf7ieZz9cx9bdhxjQqSWPXjKIQZ2PCFPniEighztFVadUXYjI20C7IEPcGnihqioi4f7D3wCcqaofisivgAdwRD0obv7vm4C+wOFvIFU9NcxxDeM7JLSAu7+wUwAKCgrqpX5j+7fn+Y82MOerYs44Ji+i9hmhsWt/GbOWbWXG51t4b8U2SisqGdYzh9+ddwyn9mmDiATrtl1Va6yCoaqn1fSeiGwVkTxV3SwieTiZAkPCFeNjVbVqJvVFYEYd3Z5z250FXAWMB7aFOqZh1ERCC3gkGNItm5xmjZi2tMgEPEaUV1Ty6aZdLFi5nfmrtlO47hvKK5X2WZlcMrQzPxyST482zaJpwjQcEZ3svr4WRt9vgCwR6aWqK4BRQF25vbNV9SkRuV5V5wJzReTj+hhuGIEkvYCnpghj+ufxr4/Ws2PvIbKbNfLGkMcf92bcGKCqrCrey7yV23l/9XY+XFPCnkPliMDR7Vsw8XvdGH10O/p3zKrJ2440k4GXRGQCsA64CEBECoCrVPUK93oe0AdoJiIbgQmq+paITAT+IyKVOIJ+eR3jlbmvm0XkLJxJT8sHbjQYiauMfA2goKBAF9WzsvvKrXs4/S/vcc2IHvzy+70jbFlyUlmpfPR1CTO/2Mrby7YeThzWJbsJJ3TP4aQe2ZzYPYfWTetX21dECmsLocQTIjIGmAd0Ah7GWcFyp6pO89QwI+FJeg8coGfb5pzRrx1T3/+aid/rRlbj9NgbIeKLScxNOw/wn8KNvFy4gQ0lB8hIS+Gk7tlcObwbw3vl0rFVHBXRiBGqWrURaBdwipe2GP7CBNzl6hE9eOOzLfzzg6+59tSeXpuTcCzbvJtH313N9E+LqFQ4qUc2vxjVm1F929K0UXL+mInIr1X1jzVt6LGNPEZDSc7frCD065DFqX3a8NT8tfzkpK5JKzrh8mXRbh6YtZy3lxXTNCOVicO6ccnQzvFVrs47qiY3bUOPERVMpQK45pQeXPDo+zz/0XquGNYttoOPGRPb8RpI8e6D/GnmCl4q3ECLzHRuHNWL8Sd0IauJB+GnOEVVX3dP96vqy4Hvicj/eWCS4TNMwAMY1LkVJ3bP5vH31nDJ0M6x3djz+ut1t4kDyisqeWr+Wh6cvZKyikomnNSVn53a04S7dm4BXg7hnmGEhQl4Na4f2ZMfTFnIn2et4JYzj4rdwGPHxr2IL92wk1te+YwvN+/mtKPacNtZfemS09Rrs+IWETkDOBPoICIPBbzVAij3xirDT5iAV2NIt2wuHpzPlHlrOP3otgzqHKPlutOn193GIw6WVXD/W8t5esFacpo14rFLBjK6n216CoEinPj32UBgyaU9ONvxDaNBmIAH4dazjuK9Fdv45cuf8sZ1w2ic4WGOFI9ZsmEnN760hDXb9nHx4HxuObMPLTItXBIKqrpURD4Hvm+JrYxoYEmwg9CsURp/vLA/a7fv4/6Zy702xxNKyyu5/63lnP+3BRworeAflw/m9+cfY+IdJqpaAXQSkfrtWDKMWjAPvAZO6pHDpUM78/SCtZzSuw0n98yJ7oBxtIlnVfFebnjRKZ5wwcCO/HZsX282N/mHtcACEZkG7Ku6qaoPeGeS4QfMA6+Fm8/oQ682zfnpc4Ws3LonuoNNmVJ3myijqvxz4TrGPDyPDd/s57FLBvKni4418W44q4HpOL9vzQMOw2gQlgulDjbtPMC5jywgIzWFV685kTbNI1JQ4Eg83kq/Y+8hbvrPp7y9rJhhPXO4//+OpW2LKH3WCJBIuVAMI1qYB14HHVo25unxx1Oyr5SJUxdxoLTCa5MiznsrtjH6wXm8t2I7vx3Tl6k/GRzX4p1oiEiuiNwnIm+IyDtVh9d2GYmPCXgIHNMxi4cuPo5PN+3iqmcLOVTuDxEvLa/k3v99yY+f/oiWjdN57dqTuPzkrqSkxCSlazLxHPAV0BW4E/gasHzgRoMxAQ+RUX3b8vvzjmHuim1c89wnlJZXRnaAabHNLLpm217Of3QBT8xbyyVD83n9ZydzVF6Lujsa9SFbVZ8CylR1rqpeDlg5NaPB2CqUMBg3OJ+yikp+89oXXP/CJzx88XGkpUboO3DQoMg8pw5UlX8XbuT2aV+QkZbC45cO4vtHBysdaUQQK+hgRAUT8DC59IQulFYod0//khteWsqfLzo2MiLeoUPUJzH3HCzj1lc/Z9rSIoZ0bc1fxg0gL6txVMc0ALhHRLKAX/BtQQfbiWk0GBPwejDh5K6UV1Ty+ze/QoAHIiXiUeST9d9w3QufULTzIL8Y1YurT+lBqsW6o4qIZOIUMe4BdACeUlUr6GBEDBPwenLl8O5UKvxhxleIwAMXDYhLQSyvqOSROat56J2VtGuRyYuThlLQxf56jxFTccIn84AzgL7A9Z5aZPgKE/AG8NMR3alU5b63liPAnxoi4hMnRtQ2gPU79nPDS0soXPcN5w5oz13n9rOt8LGlr6oeAyAiTwEfxWpgEekCTFfVfrEa04g9JuAN5JpTegBw31vLqdQGhFMiuBOzslL5xwdf84cZy0lLER4cN4BzBnSI2PONkKmavERVy0Xi7y80I7ExAY8A15zSgxQR/jDjKypU+csPBpAerogPGgSFhXW3q4PV2/Zy078/ZdG6bxjRO5ffnXcM7VvaRKVHHCsiu91zARq71wKoqkZ73WaaiDwHDAS+AH6sqvujPKYRQ0zAI8RPR3QnLUW4941lVFQoD148gEZpYaShXby4QePvO1TOX+es4ql5a8lMT+FP/3cs5w/sgHl93qGqXuch7g1MUNUFIvI0cDVwv8c2GRHEBDyCTPxeN1JThLumf8n4pz/i8UsLop4IqrJSmba0iN+/uYytuw9xwcCO3HRG7+jlbDESiQ2qusA9fxa4DhNwX2ECHmEuP7kr2c0y+OXLS7nosQ945vLjQ1trnRdehRtV5Z2virnvreV8tWUP/Ttm8eglgxiY36qelhs+pPrGAn9krjMOYwIeBc4Z0IGcZo248p+FnPfI+zw4bgBDumXX3qmoKKRnV1Yqc5YX88icVSxev5PO2U14cNwAxvZvbzlMjOrki8gJqvoB8ENgvtcGGZElvnefJDAn9cjhpStPoFF6CuOeWMg907/kYFktSbDuuKPW5x0sq+DfhRsZ/eB7TJi6iC27DvK7847h7RuHc86ADibeRjCWA9eIyDKgFfCox/YYEcbygUeZfYfK+f2by3h24Xp6tmnGz0/rxelHtz1ylUqQfOCqyhdFu3l50Qb+u6SIXQfK6NOuOVcN785Z/fPCX+niIxqSD1xEWgMvAl1wMgNepKrfBGk3AxgKzFfVMQH3T8WJJWfgFCueoKpWZd6IOSbgMWLuim3c9t/P2FBygNzmjRh3fCdO7J5D5+wmtGuRSUpqCrv2l7Jtz0G+3LyHBSu3s2D1djZ+c4CMtBRGH92Oiwo6cVKPbFtZQoMF/I9AiapOFpGbgVaqelOQdiOBJsCVVQIuIinAOmCkqq4QkbuAdW62QcOIKSbgMaSiUpm7ophnF65nzvLiww53RloKK+49ky43TT/ctnlmGid2z+Z7vXIZc0x7sprYDspAGijgy4ERqrpZRPKAd1W1dw1tRwC/DBDwXGChqnZ3r4cBt6jqmfWxxTAagk1ixpDUFOHUPm05tU9bincfZMXWvawr2cf6Hfv5+4Mvc9txR5HbvBFdspvSr0NWXOZW8QltVXWze74FaBtG3+04G2QKVHURcCHQKdIGGkYoJLSAi8gkYBJAfn6+x9aER5sWmbRpkcnJuNXuC/fDoG7eGpVY5IhI4J9cU1T1cD4CEXkbCJbo/NbAC1VVEQnpz1ARGQ08CKQCL4vINmAmUFGt3QXAv4HjVXWRm5dkGc6kIjge/FVhjPWkqk4O0uYi4A6c5YFLVfWH7v3xwG1us3tUdWp9xxGRPwNVGRSbAG1UtaX7XgXwmfveelU9uyGfSUQ6A08DuUAJcImqbgz3MyUVquqLY9CgQZrQOFpihAiwSOv5s4IjpHnueR6wvJa2I3AqyqfiVJfvhjN5uRQnu+DpwEsB7ZsD7wELgQL3Xhfg8zDsCzpWtTY9gU9w4vfgCCs4hSLWuK+t3PNW9R2nWvufAU8HXO+N8Gd6GRjvnp8K/DPcz5RsR/IuYzCSmWnAePd8PPBaCH0GA6tUdQ3QEngBOB+4CXgsoN3dwB+Agw2w7/BYqlrqjnVOtTYTgUfUXT2jqsXu/e8Ds1S1xH1vFjC6AeMEcjHwfL0+UWhj9QWqij3PCXg/nM+UVJiAG8nIZGCUiKwETnOvEZECEXmyqpGIzMPxCkfieOFVhVB/BVyLU2HndVV9x20/EOikqv8LMmZXEflEROa6E5+10QHYEHC90b0XSC+gl4gsEJGFbngi1L7hjAMcDm905VuBBcgUkUXu+OfW9oFCHGspzpciwHlAcxHJDsfOZCOhY+CBFBYWbheRdV7b0SBseWA4dK5vR1XdgSPK1e8vAq4IuD4stCJyIa7Xp6q/EpFPgSGq+hf3/RTgAeCyIENuBvJVdYeIDAL+KyJHq+ruIG1DJQ0njDIC6Ai8JyLHNOB5dTEO+LeqBsb7O6vqJhHpBrwjIp+p6uoGjPFL4K8ichlOGGoT1eYXjO/iGwFX1VyvbTB8zSa+u9qko3uviuZAP+Bdd51+O2CaiJztfjEcAlDVQhFZjeNB17Tuta6xwPFCP1TVMmCtiKzAEfRNOKIe2PfdBoxTxTjgmsAbqrrJfV0jIu8Cx+HEues1lqoW4XrgItIMuEBVd4pIOJ8pufA6CG+HHYlw4Dg7a3DCCFWTcEfX0v5dvp3EzAVS3fNuOMLVuiFj4fw1MNU9z8EJMWTjTPStxZnsa+WeBx0r1M8E9MHZsSoB91oBjQLGX0ntE6ChfKYcIMU9vxe4yz0P+TMl2+EbD9wwook6FXWuBd7CWVHxtKp+4e7EXKSq02rp/j3gLhEpw4mjX6WqJQ0c6y3gdBH5EifM8Ct1QkOIyN3Ax+7j7qpprDA+0zjgBXXV1OUo4HERqcSZS5usql828DONAH7vLut8D9fjV9WSUD9TsuGbnZiGYRjJhq1CMQzDSFBMwA3DMBIUE3DDMIwExQTcMAwjQTEBNwzDSFBMwA0jwrhZMn0zTizHiuVn8gMm4IYReWIlQrEUOz9+poTHBNwwDCNB8c1GnsCk/M0z26GNUg+/V5nuJImqDNh3Wlm9QllaJdVJTwstj06TtLIj7mmlsn35t3Vyc3q3QhpQYSec5+0vr6H8mir7VhUfvmzao02dCbTKylNrff8wlUrpus2HLzM650EUKwqVrqtIe94AACAASURBVC3aoao5obbPycnRLl26RM2eQLZt20ZubvRT80R9nKIiaN8+NmO5RGqcwsLC7ZoE+ZF8s5U+TTIo11LaZfXl2C4XcKBT88Pv7W3nfMwDbb5tf7BNtS+uNkemb26fu7POcfu3LqrxvZk3z2P1rPV0H5XP6ZPryiBaN+E+79OS9kfc++qeaeyYu5zs4b3pc1utBVS+Q9G2lnW2KX7oBfYt/IymQ4+hzXXjQn52uBQ/9AKla4uyw+nTpUsX4r1matwxaRJMmVJ3uzgk4TOThohvPPCstFw9vvlZpHV2SqtVCXiVeEMtAh4F8a6ibH8Z6REsSFyf51UX8ooDpaQ2zgh77FBEvPLgIVIyG4X97FCpPHiIdZffBYCqhuziJ0LRayNyNKTodSLhqxh4moQmbEd431EkkuJd3+dV/6Kpj3iD86VW1xdbNMW76vlNh0Yz7bVxmEGDvLbAqANfCXhtBHrfdREp7zue6N+6KGI2h/LvE03c8MwnnhqRDCxe7LUFRh0kjYDXSJDwSV0kmngHEkkR91jIj5x1NowkwwQ8TBJZvKuI5Gfw2hs3okhentcWGHXgm1UoAOQ7P3CBK1CqU1f8OxaCdHKLlXW2mb+7Z1Rt6N+6KOgqlfpQ9W8WyiSnkUAUJb6z4nd87YEHrkAJSpjhk4Z6rie3WBmSeAe2DbV9fYhkXBziIqxiRJI77vDaAk8QkdYiMktEVrqvrWpoN0NEdorI9Gr3RUTuFZEVIrJMRK6Llq2+FvAqwpnAjAYNFeJoi3mkw0JVQm5inuDceafXFnjFzcBsVe0JzHavg3EfcGmQ+5fhFHDuo6pHAS9Ew0hIEgEPldoEp74iF2nRjZaQRyu2b2JuJCDnAFPd86nAucEaqepsYE+Qt36KU7ez0m1XHKRNRPCPgGfUf7115cFDtb4fL+Jd/dmRfn60J2hNzI0Eoa2qVuWF2AK0DbN/d+AHIrJIRN4UkahNaPlrErMeBG7/bn/36Ig8M5rCXdNYkZr0jOTkZm1UF/FoTIC6qUknAeTn50f8+b4nsXeu5ohI4AeYoqqH8wKIyNtAuyD9bg28UFUNzLMUIo2Ag6paICLnA08DDc+lEYSkEvDq2+crDx5i38LPANi38DMqDpxa712KXnNyi5VRX7kSTQIFPVJi7v7CTgFnK31EHmokCttr20qvqqfV9J6IbBWRPFXdLCJ5QLghkI3AK+75q8Dfw+wfMlENoYjIaBFZLiKrROSIiQAR6Swis0XkUxF5V0Q6Brw33p0FXiki40MdM1gOlJoI3JadPbx3UPEON6wQS+872NiRGN/rte4WZokTCnyfSqQmpgFVmjMeeC3M/v8FTnHPhwMrImTXEURNwEUkFXgEOAPoC1wsIn2rNbsf+Ieq9gfuAn7v9m0N3A4MAQYDt9e0lKehtLluHJ2f/m1YmflqIlA8D+4LLRVtNPCDiINtEjI8YzIwSkRWAqe514hIgYg8WdVIROYBLwMjRWSjiHw/oP8FIvIZjqZdUdNAItJWRJ4SkTfd674iMiFUQ6PpgQ8GVqnqGlUtxVlKc061Nn2Bd9zzOQHvfx+YpaolqvoNMAuoV4A6lCWENSVgCkfEAkXzoetXMWFAIQ9dvyrk/pHGTyJuQm7EElXdoaojVbWnqp6mqiXu/UWqekVAu2GqmquqjVW1o6q+5d7fqapnqeoxqnqCqi6tZbhngLeAqomnFcDPQ7U1mgLeAdgQcL3RvRfIUuB89/w8oLmIZIfYFxGZ5M70Liot319vQyMpEAf3VfDhGyUAfPhGiXniEcJE3ANuv91rC5KBHFV9CTe3j6qWAyGLhtfLCH8JDBeRT3BiRZsIw3hVnaKqBapakJHWJPRR65HAqjYChTKzaSpDzmwNwJAzW5PZNMSKNlHCRNyoN0m6EzPG7HOdVgUQkaHArlA7R3MVyiac3UhVdHTvHUZVi3A9cBFpBlygqjtFZBMwolrfdxtiTLg5wEMVrWACed2DPTj4uwrPxbuKSKxQidXywrpon7vTcq7EivbtLR9K9LkRZ9K0u4gsAHKBC0PtHE0P/GOgp4h0FZEMYByOoYcRkRwRqbLhFpz1kuDEhE4XkVbu5OXp7r2EIV7EuwrzxI2w2by57jZGg1DVxTjRhxOBK4GjVfXTUPtHTcDdWM61OMK7DHhJVb8QkbtEpGrJxwhguYiswNntdK/btwS4G+dL4GOcbakl0bCzIWLg5ZLB+uAnETcMPyAi1wDNVPULVf0caCYiV4faP6obeVT1DeCNavd+G3D+b+DfNfR9mm898pjiZ5HyUzjFiDIDB3ptQTIwUVUfqbpQ1W9EZCLwt1A6ez2JGVHC2cTTUBLN+440fv6SM1wKC722IBlIFZHDxbnd/TMhbwf3lYBXJ+ga8AivQElEIvXlYyLucyZN8tqCZGAG8KKIjBSRkcDz7r2QSKpcKPHGsMbra3xv3oHoJl+KVO4UC6f4mCeegClT6m5nNISbcCYvf+pezwKerLn5d0kKAQ9nCWEoXmUkPNjaxDvw/WgLeSQwETeM+uHmDH/UPcLG1yGUuvBiOdqwxutrFO99+44stF6X0DeESMbxoxVOqThQGpXnGkY8ICInuWXbVojIGhFZKyJrQu2f1AJeH6I1eXn1T3dyVO9irv7pkV8qtYl+PBFpEf/qnmksPPtBvrpnWt2NjcizaVPdbYyG8hTwAHAycDxQ4L6GhG8EvDIj/j9KbZ739NedydXprx8M6onX1r8hxGtVn4oDpeyYuxyAHXOXmyfuBbYKJRbsUtU3VbXYTaK1Q1V3hNo5/lUvDijbX9bgZ9Qmvk2bpjBmbCYAY8Zm0rRpzf8tyeKJpzbOIHt4b6DmXO1GlDm74SmWjTqZIyL3icgJIjKw6gi1c1JMYoZKMOGZefM8Vs9aT/dR+Zw+OSpVkQD426Mtue/+ylrFu4phjddHdHIzGtV8qv4tGzK52ee2s6n4RamJt+FnhrivgdUzFDg1lM4m4LVQtr+M1bMcj3f1rPUU3PcVEH6Ok1C95lDEO/CZybBCxcTb8DOqekrdrWomuUIoYW7iSW+STvdRjkh2H5UfdwmqIkk0d5b2b11km34Skccf99oC3xPPFXk8oWobfV2VeEJdQnj65GFcMe+ieodPohmzToR4eCAm4gmG7cSMBc8QpxV5fEN6k3SvTYgJscjvYt54AvFtig4jeiR0RZ6EIV6TVyWaF16FCblhAHFckScuCHUbvYmJN9g2fCPJiduKPElPrLzjSI7jxV8a9uUZp4wZ47UFvqehFXl874EbiYF54nHI6697bYFvEZHza3irl4igqq+E8pyoeuAiMlpElovIKhG5Ocj7fxaRJe6xQkR2BrxXEfCeJcNIAswTjzPGjvXaAj8z1j0m4ORD+ZF7PAlcHupDouaBu5UlHgFGARuBj0Vkmqp+WdVGVW8IaP8z4LiARxxQ1QHRsi8c4nUCM1pEY2dmqJgnHkdMn+61Bb5FVX8CICIzgb6qutm9zsNZWhgS0fTABwOrVHWNqpYCLwDn1NL+YpxqFAnFwX0hr/iplZSS4AmsQqU+cfBI2W4YRr3pVCXeLluBkLdYR1PAOwAbAq43uveOQEQ6A12BdwJuZ4rIIhFZKCLnRs/M+vPQ9auYMKCQh65f1eBntfzN7ghYFDqRtD3SWCjFSCJmi8hbInKZiFwG/A94O9TO8bIKZRzwb1UNdAk7q2oB8EPgLyLSvXonEZnkivyistJ9sbIVcLzXD98oAeDDN0qO8GaDecQ1pYlNW1NO09cOkramnH37KikuLo+IfbW9V5vt8YCJeBygoVey8hMi0totsrDSfW1VQ7sZIrJTRKZXuz8vYP6uSET+W9NYqnot8DhwrHtMUdWfhWprNAV8E9Ap4Lqjey8Y46gWPlHVTe7rGuBdvhsfr2ozRVULVLUgPaNpJGwOmcymqQw5szUAQ85sXWeelO8UbKhUZF/l4aPJNCdHy+yJOynoXczwgds5pWArVNbvF6gu7zoU25Mt7m8EIXnrYd4MzFbVnsBs9zoY9wGXVr+pqsNUdYA7h/cBUOuKElV9RVVvcI9XwzE0mgL+MdBTRLqKSAaOSB+xmkRE+gCtcD5o1b1WItLIPc8BTgK+rN43LAISWUWqlNp1D/bgqSWDuO7BHrW2O6Jgw95Kmj+2jw59i+nYu5is+/cCcMnycvYCO4EfbVGKt4bviYfqXYdqu5HEXHml1xZ4xTnAVPd8KhA0hKuqs4E9NT1ERFrgpIWt0QMXkfNdT3+XiOwWkT0iEnI8NWoC7u7pvxYnUcsy4CVV/UJE7hKRwEzx44AXVL/z99pRwCIRWQrMASYHrl6JJ0LJUHhEwYYWqez+RXO2vdiasrzv/hesB04BnmifQpu80HOwVKWWDecvg3jPrtjQMEpgiG3btm0RsspIEHKq/u/dI5zMXG0DJha3AG3racO5OJ58bYL8R+BsVc1S1Raq2lxVW4Q6QFQ38qjqG8Ab1e79ttr1HUH6vQ8c09DxK0sPAXXnk45FvDVYwYZDQzPYd3lTWt777Zd45q+b8ZdxmbRpU///muse7MHB31XEvUBHG1WdAkwBKCgoSM6AbvKy3Z1DC4qIvA20C/LWrYEXqqoiUt+fnYtx1nXXxlZVXVbP5/t3J+bGV//BnmVLaDrgWNpc9mOvzQGCF2xoPOMglZmw69zGZL16gKx3DnHoumYNHisS4u3lenAjDpjm3/1zqnpaTe+JyFYRyVPVze667OJwn++GfgcD59XRdJGIvIgTZjkUYJ/3OzG9oqLsEHuWLQFg35KlVB46VEcPb0jdXEHRinL6H4Qf71OK/5eN7FVStsTfqhAjCRk0yGsLvGIaMN49Hw+8Vo9nXAhMV9W6qsi0APYDp/Pt7syQk9D40gNPTW9E86MGHPbAUxo1AsKrxlNFNFdj7N9TyTF7lIPAF68fZOf9LSh/PbvBm3r8hO3M9JAOHZJ1KeFk4CW3Ms464CIAESkArlLVK9zreUAfoJmIbAQmqOpb7jPGuc+plaodmfXFlwIO0PG8H1N51g8o7Ri/NRUb9UrntLGZTH/94OFq9ApUdAgv/JEItTENI1FQ1R3AyCD3FwFXBFzXWKZLVUeEMpaI9AIexZk47Sci/XEmNe8Jpb8vQyhVpGQ08tqEOvnboy1ZtrwNf3u0pdemGIYRe54AbgHKANxUsuNC7exrAfeSYF5xTTsxw6lGH8o4hhERJk702oJkoImqflTtXsgbQEzAY8R3dmIaRiKQvDsxY8l2N01IVUm1C4HNtXf5FhPwGHDETswaPHHDiCuSdxVKLLkGJxdKHxHZhFOR/qpQO5uAx4AjdmI2IGQSiIVPjKiyeLHXFvgeN932aTi1MPuo6smqui7U/r5dhRJvBNuJaRhGcuNWpL8dOBlQEZkP3OWuhKkTU5M6iOROxEiKt3nfRtTJy/PagmTgBWAbcAHO5p9twIuhdg5JUUQkU0RuFJFXROQ/InKDiGTWy9wkIloia+JtxIQiy8keA/JU9W5VXese9xBG8qxQXcJ/AEcDDwN/BfoC/wzbVMMT4rFgg5EA3HGH1xYkAzNFZJyIpLjHRTgZXEMiVAHvp6oTVHWOe0zEEXQjxoTrfcdz6bRQsG30HnLnnV5bkAxMBP4FlOIks3oBuDLUvOChCvhiERladSEiQ4BF9TA26fAy3JEIpdMMI5lx83+nqGqaqqa7581DzQteq4CLyGci8ikwCHhfRL4WkbU41XNqzLVrRIdwvwzCLftWHUslaxjRRRwuEZHfuNedRGRwqP3rWkYYclpDI7rU15O34g5GvVlkf2THgL8BlTil1+4G9gKPAMeH0rlWAQ9nQblRM/MO5AetUh9O/4bgN/GuOFDqtQmGESmGqOpAEfkEQFW/cWsIh0RU14GLyGgRWS4iq0QkaGVnEblIRL4UkS9E5F8B98e7xT5Xisj4YH2N6BEP4ZNgE5hf3TONhWc/CNAt5gYlGwUWJY0BZSKSyre5UHJxPPKQiJqAu0Y9ApyBs+zwYhHpW61NT5xUiiep6tE4eQAQkdY4u5OG4JQlul1EWkXL1lisdKivF21rvr+l4kApO+Yur7qM2s+DYcSQh4BXgTYici8wH/hdqJ2j6YEPBla5e/1LcZbHnFOtzUTgEVX9BkBVq2rPfR+Ypaol7nuzgNFRtLVWIuWNhivGXol3Qz9v2f6yCFnyXVIbZ5A9vHfV5TdRGcQwYoiqPgf8Gvg9ThbCc1X15VD7R1PAOwAbAq43uvcC6QX0EpEFIrJQREaH0RcRmSQii0RkUVnpvgia7j2J6nnPvHkeTw57iZk3z2vQc2r6q6jPbWczdNr1AGsaNIBRN7ff7rUFvkVEWlcdOEWTn8dZD77VvRcSXiezSgN6AiOAjsB7InJMqJ1VdQowBaB5y44JUbyvSphrm9T0Urwb4n2X7S9j9Sznc62etZ6y35aR3iQ9UqYdJrVx/JbJ8xW2EzOaFOLEvQXIx/mLUoCWwHqgaygPiaYHvgnoFHDd0b0XyEZgmqqWqepaYAWOoIfSN6GpLtLzDuQfPryioaGT9CbpdB/l2N99VH69xdt2X8YJ7e3/IVqoaldV7Qa8DYxV1RxVzcZZuj0z1OdE0wP/GOgpIl1xxHcc8MNqbf4LXAz8XURycEIqa4DVwO8CJi5Px5nsjAhF21rSPtf7yjixFuuD+2peDx6pOP/pk4dFzfM2YszmkAvDGPVnqJuaBABVfVNE/hhq56h54KpaDlyLk5hlGfCSqn4hIneJyNlus7eAHSLyJTAH+JWq7lDVEpxF7R+7x13uvbCoLD307UVxw5InxsOyulCoabt8bTlRIv3ZGiLe5n0bSUaRiNwmIl3c41Yg5DSQUY2Bq+obwBvV7v024FyBG92jet+ngafrO/bGV//BnmVLaDrgWNpc9uP6PiaheOj6VXz4RglDzmzNdQ/2OHz/iJwo7s7MRPlSMjxi4ECvLUgGLsZZMv0qTkz8PfdeSHg9iRkVKsoOsWfZEgD2LVlK5aFDpDRqFNUxawtPxIKaRBq+zYlSJe6LKvpAnXnOYo9533FGYaHXFvgeN7JwfX37+7IiT2p6I5ofNQCApgOOjbp4Rzpla32yBtaVuOq6B3twxbyLGHj3GRGxMdKYeMchkyZ5bYFRB770wAE6nvdjKs/6AaUdI7fkbP7unpzcYuV37tXm+daHmsIgoVBb4qr5u3uS3qTeZhnJyBNPwJQpXlth1IIvPfAqUjKi63lDw1O2BhKJ/N3Vx5+/u2fcx7rN+zbiCXeDzSw3D9OsmtJ4iMgMEdkpItOr3R8pIotFZImIzBeR8DyxMPCtBx5LIpWytXqsuiHPi3fRrsLE24hDbgZmq+pkNwnfzcBNQdrdBzQBrqx2/1HgHFVdJiJXA7cBlwU2EJGHcRNYBUNVrwvFUBNwl09L2tO/df2LuEZqAjMSXwaJIt6xpEITYqNufLHJV3vnwuEcnN3hAFOBdwki4Ko6W0RGVL+PI8xV1XSyCL4sMCLJ1k3AwyRYHDzSJIt4x9L7XrFlD//7dDNnHtMOEYnZuAlNYWGy7sZsq6pVu5i2EEaVeJcrgDdE5ADOeq+h1Ruo6tSGmehgAu4jTLy/i4hMAiYBNM3rzjX/WsyI3rncfU4/OrW2Gd06OftsSNy/XHJEJNDLneLmTgJARN4G2gXpd2vghaqqiIT7j3ADcKaqfigivwIewBH1I3Dzf9+Ek3L78G5DVT01lIF8PYkZLeJRKOPRppqIleetqlNUtUBVC3q3b8lvx/Tl47UljPrzXB59dzVlFSHnzTcSj+1V//fu8Z3lNKp6mqr2C3K8hpMRMA/AfS0ONkAwXEE+VlU/dG+9CJxYS5fncHaqdwXuBL7G2X0eEibgPiCRxNsrBLj85K68/YvhDO+Vyx9mfMWYh+ZTuM7SihtHMA2oqgI2HngtjL7fAFki0su9HoUj0DWRrapPAWWqOldVL8epjxkSvhfwzOLoxDvjRTTjxY5Q8XrVSV5WYx6/tIAnflzAnoNlXPDo+9z66mfsOhCdIhQJzeOPe22BV0wGRonISuA09xoRKRCRJ6saicg84GVgpIhsFJHvuzmgJgL/EZGlwKXAr2oZq+oHb7OInCUixwEJkw/cM+IlI2FDMPGuP6P6tuXE7tk8MGsFf1+wlplfbuX2sX0565g8m+SsIkl3YqrqDmBkkPuLCIhlq+qwGvq/ipPbJBTuEZEs4BfAwzirV24I1Vbfe+DRxEsBNfFuOE0bpfGbMX2Zdu3JtGuRybX/+oTLn/mYDSX7vTYtPrAvsqijqtNVdZeqfq6qp6jqIFWdFmp/E/AA4lFkgpFo4h3v9OuQxatXn8hvxvTlw7UlnP7n95jy3mrKbZIz4SirqOTWVz/z2ow6EZFfu68Pi8hD1Y9Qn+MrAa8oP1R3owgTazFNRPFOhC/GtNQUJpzclbdvHM5JPXL43RtfcfZfF7BkQ2KH2ZIJVeWWVz7juQ9rLlcYR1RNbC7CKa9W/QgJ3wj4wf0lLJjxGzb87xkAGgdb+NPAog5eY+Idfdq3bMwTPx7EY5cMZMe+Q5z3twXc/trn7DmYhJOcY8Z4bUFYPDBrBf8u3Mj1I+P/90RVX3dP96vq1MADCDmG5xsBLy87AMC2zZ9SURZbTzwWwmriHTtEhNH98nj7xuGMP6EL/1i4jpF/mssbn21GE3djS/i8/nrdbeKEZxeu4+F3VvGDgk78/LSE+l0JVioy5PKRURVwERktIstFZJWbFKamdheIiIpIgXvdRUQOuNm8lojIY3WNlZbeGIDcvP6kpkc/C2F1oimwJt7e0DwznTvOPppXrz6JnGaNuPq5xck1yTl2rNcWhMSbn23mt699zim9c7n3vH4JsYpIRM5wE1p1qBb/fgYoD/U5UVtGKCKpwCM4C9k3Ah+LyDRV/bJau+Y4FSk+rPaI1ao6INTxMpu05tgTriI1rRF7G2h7fYlGnpREFO9oU7StZUzHG9CpJdOuPYln3v+aB2atYNSf53LdyJ5ccXI3MtJ880fskUyfXncbj5mzvJjrXviEAZ1a8siPBpKWmjD/H0U48e+z+W7Mew9xsoxwMLBKVdeoainwAk6Wr+rcDfwBONjQAVPTGu55N9RzjKTgJqp4R9P7jrV4V5GWmsIVw7rx9o3DGdGrDX+csZyzHprHwjU7PLHHgIVrdnDVPwvp1bY5f//JYJpkJM62FlVdCjwLLKgWA39FVUPeHhxNAe8AbAi43ujeO4yIDAQ6qer/gvTvKiKfiMhcEQm6YD5eiYTwmngfiVfiHUj7lo157NJBPDW+gP2lFYybspAbX1zCtj2xXwGVzHyy/hsmPPMxnVo34R+XDyarcbrXJoWNqlYAnUSk3mXDPPvKEpEUnCxdlwV5ezOQr6o7RGQQ8F8ROVpVd1d7xuFsc40ae//LHUhDwikm3vHPyKPacmL3HP46ZyVT3lvDrGVb+eXpvblkaGdSU+I/BhsScTphW7iuhPFPf0xO80Y8O2EI2c1iP+cVQdYCC0RkGrCv6qaqPhBK52h64JuATgHXHd17VTQH+gHvisjXODlzp4lIgaoecrezoqqFwGqgF9UIzDaXntE0bAOj7dGFK8Tzd/dkzpYu0TEmwYkH77s6jTNS+dX3+/Dm9d+jf8csbp/2BWMfns+ir0u8Ni0yxGE9zI/WlvDjpz4it3kjXpx0Au2yEntpMI62TcfR4uYBR0hE0wP/GOgpIl1xhHsc8MOqN1V1F5BTdS0i7wK/VNVFbkrGElWtEJFuQE9gTRRtjRpVIl6XNz5/d09m3jyP1bPW031UPqdPTqioke9DJ7XRo00znp0whP99tpl7/7eMCx/7gPOP68DNZ/ShTYsEFpgrr4yrfCjvr9rOhKmLaN8yk+cnDk3sf1sXVb2zIf2jJuCqWi4i1wJvAanA06r6hYjcBSyqY7//94C7RKQMqASuUtVa3ZqU0vje9lyXN162v4zVs5wdZKtnrafst2WkN0mMuF4yhU5qQkQY0789p/RuwyNzVvHEvDW89cUWrj21J5ef3IVGaZEpuZes/O/Tzdzw4hK65DThuSuGkts8ocMmh3Gd1V8DRxNvBR1U9Q1V7aWq3VX1Xvfeb4OJt6qOcLN9oar/UdWjVXWAqg4M2LVUL8JNKeuFIKU3Saf7qHwAuo/KN/F2iXfvuzpNG6Xx69F9mHXDcE7onsMfZnzF6X9+j7e+2JJcm4AiyD8/+Jprn19M/45ZvHTlCb4Rb5fngK+oZ0GHxFl3kwScPnmYp5532f7wxjbxrpkuOU15cnwBc1ds4+7pX3LlPwsZ0rU1vxnTl34dsrw2LzSmhZwULypUVioPzFrBX+es4rSj2vLXHx5HZrrv/pLJVtWnROR6VZ0LzBURq8iTqHgl3jNvnseTw15i5s3zQmpv4h0aw3vl8ub1w7j7nKNZWbyXsX+dz40vLWHTzgNem1Y3gwZ5NvT+0nKufm4xf52ziosHd+KxSwb6UbzBCjqESXEmtAlvz1C4nmmiEW783cQ7PNJTU7j0hC6cPaADf5uzir+//zXTP93MZSd24ZoRPciK15+tDh08WUq4aecBJk5dxFdbdvObMX25/KQuCbE9vp5YQYdoUCVS4XqmiUg48XebsKw/WY3TueXMo3jnF8MZ0z+PJ+atYdgf3+Gv76xk36GQ01/4mvdWbOPsh+ezoWQ/T192PBNO7upL8RaRTBH5OTAaZ4XeV/Up6OBrD7xxMRxoU3ub2kqrJfLKEPiu2PZvXVRr21Di77EQb79538Ho2KoJD1w0gInDuvGnmcu5f+YK/r7ga346ojuXDO3s11BBrZRXVPLArBX87d3V9G7bnEd+NJAebZp5bVY0mYoTPpkHnAH0xckJFRa+FvCGUuWZVq3NTgTxrklkq98PJug1fb5Yed3JIN6BHJXXgifHH8/i9d/wp5nLued/y3hs7hqu/F431QjExwAACjBJREFUfjQ03/vcHhMnxmSY9Tv284uXl/Dx198w7vhO3D72aBpn+P5LrK+qHgMgIk8BH9XnISbgdeD1ypBQCVdkQxH0WIZLkk28AxmY34rnrhjKh2t28NA7K7n3jWU8Nnc1l53YhUtP6EzLJvVOldEworwTs7JS+ccHX/OHGctJSxH+8oMBnHtchzr7+YTDFULcPTP1eogJeC18WtKe/q2LfCfe0XpGfUlm8Q5kSLdsnuuWTeG6Eh5+ZxV/mrWCR+eu5uLB+fzkpC50bNUktgYNGgSFIVf3CouVW/dw638/56O1JQzvlcvkC44hL6txVMaKU44VkarcTgI0dq8FUFVtEcpDfCXgjTfs4UCn5jTbUs7edr76aEHxw4SiifeRDOrcmmd+Mphlm3fz+NzVPPP+1/x9wVpO79uOy07qwpCurWMzsbd4ccQfuWt/GX9+ewX/XLiOphmp/PHC/vzfoI6+nKisDVWNSIzI/yrnU0y8/c9ReS34y7jj+NXoPjy7cB3Pf7SeGV9soVfbZow7Pp/zB3bwLrwSJvtLy/nXh+t5ZM4qdh0o4+LB+dw4qleiZxL0HBPwBMTEO7no0LIxN43uw3Wn9uS1JZt4/qP13DX9SybP+IrvH92O845rz7CeuaRHuhpNXl6DH7HvUDnPLlzHlPfWsGNfKSf1yOb/nXkUR7dPkN2ocY4JeB1UxcHjBRPv5KVxRirjBuczbnA+Xxbt5oWP1/P60iJeX1pE66YZnNGvHaP7tWNI1+zIlHorqv/P/Yqte3h24TpeWbyJvYfKGdYzh+tG9uT4LiFvMjRCIGkEPLNYONjG3VVWbTdmbWvB4wk/iLcRGfq2b8Fd5/TjtrP68t6Kbfx3ySZeWbyJ5z5cT/PMNEb0bsOwnjkM65lT/8nBO+5wjhDZtPMAb362mTc+28zi9TvJSE3hrP55/PiEzhyX36p+Nhi1kjQCnuj4RbzN+44sGWkpnNa3Laf1bcvBsgrmr9zOzC+38M5Xxby+1PGgu+U0ZVDnVgzIb8mATi3p2aZ5aB76nXfWKuC7DpSx6OsSFq7ZwQdrdvD5JmdRRd+8Ftx8Rh/+b1DHhIxxi0hr4EWgC052wIuC1akUkRk4hWjmq+qYgPunAvcDGTgFiyeoalS22pqAh4DXYRQT72oUJ34i/2iQmZ56WMxVleVb9zB/5XY+WL2D2V8V83LhRgBSU4TO2U3o2aYZnVo1oV1WJm1aZNK6SQaNM1JpkpFKeqrQA1iyYSf7S8vZvreUbXsOsWXXAVYW72XFlj0U7XL+is1ITeG4/Jb8enRvzuyXR5ec8KtjxRk3A7NVdbKI3Oxe3xSk3X1AE+DKqhtuqcipwEhVXeHWPxgPPBUNQ03A4xwT72qYeIeEiNCnXQv6tGvBFcO6oaqsL9nPkg07Wbl1LyuL97CqeC9zV2zjYFnwYihfA+c+suA79zLSUuie24zBXVvTs21zBua34rj8ln7b/n8OMMI9nwq8SxABV9XZIjKi2u1soFRVV7jXs4BbMAH3llh74X4RbvBOvAOLXufn50fGhgRFROic3ZTO2d/1jlWV3QfL2br7IDv3l7G/tJwDpRWUVSrvHzeDp48dQGZaKrnNG5HbvBFZjdMTZc12jogsCrieoqqhbi1tq6qb3fMtQNswxt0OpLm1fRcBF/Ld2sARxQQ8DjHxjgzuL+wUgIKCAiuHEwQRIatxOlmNg+w2Lt8MfcLRrrhiu6oW1PSmiLwNtAvy1q2BF6qqIhLyz47bfhzwZxFpBMwEKkLtHy4m4P+/vbsLkaoO4zj+/SmWIkilSQpRRFhUN4UViVHhSwgRZkEXGUUFdeFFGXVVBGKUN0ESoVBgL4QUBGkIEeVFUVGWtb1oCIEpWmtZyVbbmv66OGejjX2Z2T1nx7P+PjezZ2fnef67O/PMn/858/zbMB6z8BTvIWTpZPzNn9+RfuDjwfbioe6T9KOkObYPSpoDdLcZ+0Pg6jLWUmDemAY7jPQDL+3fV38fhqN/HB32/tEU72N/9o12OLXmGql4H+/9q/XEgxfvPHejLlsoTjxS3r7RzoMlzS5vT6VYO99Q6ej+Y8K/CKa18N7ZvX4ze+9aw+61I/dRH+0MebiNIboOzx1V3N1rt/DRjU+3NO6xaifXSMW7/+/dvX7zyIkHKd7dm14EuHTkB0eMypPAEkl7gMXlMZLmS3qu/4ckvQe8BiyStF/S9eVdD0naBXQBW22/W9dANVF2ypZ0CNg7iodOYmAx2AkMfloeZlGcpDgRcrQTc7Q5RptruPhjGfeAx9tu+WzaGJ4f0Uzn2D6z04Oo24Qp4ONB0o7hTowkx/jEj4jChF9CiYiYqFLAIyIaKgW8PfXuMTVxcozH7xBx0ssaeEREQ2UGHhHRUCngUTk1pFlGRNOlgJ8EJF0s6RpJM2vMsVDS7fBvP4gU8YiapRdKCyRNtl1LQxpJ5wOnAV/abuPz5S3HXwasA74Dpki62/YPFcafRNETeWNxqOm2N5RFfJLtdj6kExFtyAx8GJLmAdg+JqnyhseSbgBep2gMv6k/X4XxrwWeBu6xvRzoAy6pMoft47Z7KPomPw8skPRA/31V5oqIgVLAh1AW188lvQLVF3FJCygK9x22rwN+odj5o0o/Avfa/ljSWcCVwCpJGyXdUvEyx98UfY9fAK6Q9JSkJ1TI8yyiBnlhDULSdGAVcD/QJ+llqGUmvs72zvLrx4Azyg5mlbC9y/b28vBu4NlyJv4hRaP5WVXloujY9oPtd4AdwH3ADBcyE4+oQa4DH4KkucARYCpFO8he2ysrjD8ZmG77SPn1HGArsNT2IUkzbf9cVb5B8m8DHrH9WUXx5gKPAx8ADwMvAZcDb9reWEWOiBgoM/Ah2D5gu8f2TxSblk7rn4lLukzShWOMf8z2kfJQwK/A4bJ43waslVRJk/L/L5VIuplim6jKdqewfQDYBzwKrLa9BngK2FZVjogYKDPwFkmaRbFmfRUwGbjO9v6Kc2wCDgJLgTttf1lx/FOBlcBq4FbbX1Uc/2xgtu1Py+NchRJRo1xG2CLbP0nqApYBS6os3uUMeQrFNkxTgEW291QV/z+OU7xBrLD9bdXBbe8D9klS1r4j6pcZeIsknQ68Cjxou6umHHcCn9j+uo74ETGxpIC3QdJU2701xpfzD4mIFqWAR0Q0VK5CiYhoqBTwiIiGSgGPiGioFPAGkXSupK/+972ZkrZL6pH0TKfGFhHjL9eBN18vxacfL6HiToMRcWLLDLyhJJ0naSdwke33KQp5RJxEMgNvIEkXAJspPm7/RafHExGdkQLePGdStG5dYfubTg8mIjonSyjN8xvwPbCw0wOJiM7KDLx5+oCbgLck9dh+pdMDiojOSAFvINu/l1u+vS2pB1gPzABOkbScYlOILK9ETHDphRIR0VBZA4+IaKgU8IiIhkoBj4hoqBTwiIiGSgGPiGioFPCIiIZKAY+IaKgU8IiIhvoHF58Gn+H1zUcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for field, (_, _, _, metadata) in results.items():\n",
    "    _ = plot_objective(metadata, sample_source='result')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dev set evaluation: url\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.2201\n",
      "Dev set evaluation: title\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.2354\n",
      "Dev set evaluation: title.bigrams\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.1272\n",
      "Dev set evaluation: body\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.2643\n",
      "Dev set evaluation: body.bigrams\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.2045\n",
      "Dev set evaluation: expansions\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.3213\n",
      "Dev set evaluation: expansions.bigrams\n",
      "Evaluation with: MRR@100\n",
      "Score: 0.2832\n",
      "CPU times: user 14.1 s, sys: 3.68 s, total: 17.8 s\n",
      "Wall time: 13min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "_ = for_all_fields(\n",
    "    \"Dev set evaluation\",\n",
    "    fn=lambda templates, similarity: evaluate_mrr100_dev_templated(es, max_concurrent_searches, index, templates, template_id, query_params),\n",
    "    existing_results=results,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params = [(x, best) for x, (_, best, _, _) in results.items()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('url', {'k1': 0.33066956222950633, 'b': 0.9589101032169087}),\n",
       " ('title', {'k1': 0.34885436112727763, 'b': 1.0}),\n",
       " ('title.bigrams', {'k1': 1.5077266504072955, 'b': 1.0}),\n",
       " ('body', {'k1': 3.4418843002060036, 'b': 0.8106120134638413}),\n",
       " ('body.bigrams', {'k1': 1.9100199633100623, 'b': 0.7336619962002098}),\n",
       " ('expansions', {'k1': 5.0, 'b': 0.9190886566031851}),\n",
       " ('expansions.bigrams', {'k1': 1.8413006685653024, 'b': 0.8445599330786397})]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting best params for each field\n",
      "Setting BM25 params fields:\n",
      " - url: {'k1': 0.33066956222950633, 'b': 0.9589101032169087}\n",
      " - title: {'k1': 0.34885436112727763, 'b': 1.0}\n",
      " - title.bigrams: {'k1': 1.5077266504072955, 'b': 1.0}\n",
      " - body: {'k1': 3.4418843002060036, 'b': 0.8106120134638413}\n",
      " - body.bigrams: {'k1': 1.9100199633100623, 'b': 0.7336619962002098}\n",
      " - expansions: {'k1': 5.0, 'b': 0.9190886566031851}\n",
      " - expansions.bigrams: {'k1': 1.8413006685653024, 'b': 0.8445599330786397}\n"
     ]
    }
   ],
   "source": [
    "set_bm25_params(es, index, best_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "If you'd like to reset all parameters to their defaults, you can do so with the following code. However if you want to stick with the optimal values as shown above, you should skip running this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('url', {'k1': 0.33066956222950633, 'b': 0.9589101032169087}),\n",
       " ('title', {'k1': 0.34885436112727763, 'b': 1.0}),\n",
       " ('title.bigrams', {'k1': 1.2, 'b': 0.75}),\n",
       " ('body', {'k1': 3.0128735487205525, 'b': 0.8200709176657588}),\n",
       " ('body.bigrams', {'k1': 1.9100199633100623, 'b': 0.7336619962002098}),\n",
       " ('expansions', {'k1': 4.870954366799399, 'b': 0.9249613913608172}),\n",
       " ('expansions.bigrams', {'k1': 1.2, 'b': 0.75})]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best params from all previous runs with current fields and analyzers\n",
    "\n",
    "[\n",
    "    ('url', {'k1': 0.33066956222950633, 'b': 0.9589101032169087}), # 0.2201\n",
    "    ('title', {'k1': 0.34885436112727763, 'b': 1.0}), # 0.2354\n",
    "    ('title.bigrams', {'k1': 1.2, 'b': 0.75}), # 0.1295\n",
    "    ('body', {'k1': 3.0128735487205525, 'b': 0.8200709176657588}), # 0.2645\n",
    "    ('body.bigrams', {'k1': 1.9100199633100623, 'b': 0.7336619962002098}), # 0.2045\n",
    "    ('expansions', {'k1': 4.870954366799399, 'b': 0.9249613913608172}), # 0.3220\n",
    "    ('expansions.bigrams', {'k1': 1.2, 'b': 0.75}) # 0.2837\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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